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-rw-r--r--src/llama.cpp56
1 files changed, 48 insertions, 8 deletions
diff --git a/src/llama.cpp b/src/llama.cpp
index d9eec461..cae91619 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -15716,7 +15716,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
}
}
} else if (name.find("attn_v.weight") != std::string::npos) {
- if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
+ if (qs.params->attn_v_type < GGML_TYPE_COUNT) new_type = qs.params->attn_v_type;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K) {
@@ -15775,7 +15776,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
}
++qs.i_attention_wv;
} else if (name.find("attn_k.weight") != std::string::npos) {
- if (qs.model.hparams.n_expert == 8) {
+ if (qs.params->attn_k_type < GGML_TYPE_COUNT) new_type = qs.params->attn_k_type;
+ else if (qs.model.hparams.n_expert == 8) {
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
// TODO: explore better strategies
new_type = GGML_TYPE_Q8_0;
@@ -15787,7 +15789,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type = GGML_TYPE_IQ2_S;
}
} else if (name.find("attn_q.weight") != std::string::npos) {
- if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+ if (qs.params->attn_q_type < GGML_TYPE_COUNT) new_type = qs.params->attn_q_type;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
new_type = GGML_TYPE_IQ3_XXS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
@@ -15796,7 +15799,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
} 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;
+ if (qs.params->ffn_down_type < GGML_TYPE_COUNT) new_type = qs.params->ffn_down_type;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
}
@@ -15843,7 +15847,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
}
++qs.i_ffn_down;
} else if (name.find("attn_output.weight") != std::string::npos) {
- if (arch != LLM_ARCH_FALCON) {
+ if (qs.params->attn_output_type < GGML_TYPE_COUNT) new_type = qs.params->attn_output_type;
+ else if (arch != LLM_ARCH_FALCON) {
if (qs.model.hparams.n_expert >= 8) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_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 ||
@@ -15866,7 +15871,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
}
}
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) {
+ if (qs.params->attn_qkv_type < GGML_TYPE_COUNT) new_type = qs.params->attn_qkv_type;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M ) new_type = GGML_TYPE_IQ4_K;
@@ -15876,7 +15882,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
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_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+ if (qs.params->ffn_gate_type < GGML_TYPE_COUNT) new_type = qs.params->ffn_gate_type;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL && use_more_bits(i_layer, n_layer)) {
@@ -15887,7 +15894,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
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_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+ if (qs.params->ffn_up_type < GGML_TYPE_COUNT) new_type = qs.params->ffn_up_type;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL && use_more_bits(i_layer, n_layer)) {
@@ -16323,6 +16331,30 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
new_type = params->output_tensor_type;
}
+ if (params->attn_q_type < GGML_TYPE_COUNT && strcmp(tensor->name, "attn_q.weight") == 0) {
+ new_type = params->attn_q_type;
+ }
+ if (params->attn_k_type < GGML_TYPE_COUNT && strcmp(tensor->name, "attn_k.weight") == 0) {
+ new_type = params->attn_k_type;
+ }
+ if (params->attn_v_type < GGML_TYPE_COUNT && strcmp(tensor->name, "attn_v.weight") == 0) {
+ new_type = params->attn_v_type;
+ }
+ if (params->attn_qkv_type < GGML_TYPE_COUNT && strcmp(tensor->name, "attn_qkv.weight") == 0) {
+ new_type = params->attn_qkv_type;
+ }
+ if (params->attn_output_type < GGML_TYPE_COUNT && strcmp(tensor->name, "attn_output.weight") == 0) {
+ new_type = params->attn_output_type;
+ }
+ if (params->ffn_gate_type < GGML_TYPE_COUNT && strcmp(tensor->name, "ffn_gate") == 0) {
+ new_type = params->ffn_gate_type;
+ }
+ if (params->ffn_down_type < GGML_TYPE_COUNT && strcmp(tensor->name, "ffn_down") == 0) {
+ new_type = params->ffn_down_type;
+ }
+ if (params->ffn_up_type < GGML_TYPE_COUNT && strcmp(tensor->name, "ffn_up") == 0) {
+ new_type = params->ffn_up_type;
+ }
// If we've decided to quantize to the same type the tensor is already
// in then there's nothing to do.
@@ -16726,6 +16758,14 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
/*.output_tensor_type =*/ GGML_TYPE_COUNT,
/*.token_embedding_type =*/ GGML_TYPE_COUNT,
+ /*.attn_q_type =*/ GGML_TYPE_COUNT,
+ /*.attn_k_type =*/ GGML_TYPE_COUNT,
+ /*.attn_v_type =*/ GGML_TYPE_COUNT,
+ /*.attn_qkv_type =*/ GGML_TYPE_COUNT,
+ /*.attn_output_type =*/ GGML_TYPE_COUNT,
+ /*.ffn_gate_type =*/ GGML_TYPE_COUNT,
+ /*.ffn_down_type =*/ GGML_TYPE_COUNT,
+ /*.ffn_up_type =*/ GGML_TYPE_COUNT,
/*.allow_requantize =*/ false,
/*.quantize_output_tensor =*/ true,
/*.only_copy =*/ false,
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-18 10:44:17 +0000