ikawrakow/ik_llama.cpp
1
0
Fork 0
mirror of https://github.com/ikawrakow/ik_llama.cpp.git synced 2026-01-26 17:20:01 +00:00
Code Issues Packages Projects Releases Wiki Activity

CLI - Specify GGML_TYPE to quantize for the main tensors. (#91)

Browse Source 
To complement the token_embd.weight and output.weight :

attn_v.weight
attn_k.weight.
attn_q_weight
attn_output.weight
attn_qkv.weight
ffn_gate
ffn_down
ffn_up
This commit is contained in:
Nexes the Elder
2024-10-18 09:48:15 +02:00
committed by GitHub
parent f369c6f921
commit 2b1af6bade
3 changed files with 125 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files

74
examples/quantize/quantize.cpp
View File

@@ -109,19 +109,35 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
//
[[noreturn]]
static void usage(const char * executable) {
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--attn-q-type] [--attn-k-type] [--attn-v-type] [--attn-qkv-type] [--attn-output-type] [--ffn-gate-type] [--ffn-down-type] [--ffn-up-type] [--keep-split] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
printf(" --keep-split: will generate quatized model in the same shards as input");
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor.\n");
printf(" --token-embedding-type ggml_type: use this ggml_type for the token_embd.weight tensor.\n\n");
printf("Additional specific tensor quantization types used in the custom quant scheme 'CQS (default is Q2_K):\n");
printf(" --attn-q-type ggml_type: use this ggml_type for the attn_q.weight tensor.\n");
printf(" --attn-k-type ggml_type: use this ggml_type for the attn_k.weight tensor.\n");
printf(" --attn-v-type ggml_type: use this ggml_type for the attn_v.weight tensor.\n");
printf(" --attn-qkv-type ggml_type: use this ggml_type for the attn_qkv.weight tensor.\n");
printf(" --attn-output-type ggml_type: use this ggml_type for the attn_output.weight tensor.\n");
printf(" --ffn-gate-type ggml_type: use this ggml_type for the ffn_gate tensor.\n");
printf(" --ffn-down-type ggml_type: use this ggml_type for the ffn_down tensor.\n");
printf(" --ffn-up-type ggml_type: use this ggml_type for the ffn_up tensor.\n\n");
printf(" --keep-split: will generate quantized model in the same shards as input\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n\n");
printf("Note: --include-weights and --exclude-weights cannot be used together\n");
printf("Note: The token embeddings tensor is loaded in system RAM, even in case of full GPU/VRAM offload.\n");
printf("Note: The recommanded type for the output tensor is q6_K for the ffn types > iq3_xxs and < q8_0.\n\n");
printf("Note for the Custom Quant Scheme FTYPE:\n");
printf(" Write the specific tensor legacy quants as qN_N, the K-Quants as qN_K, the IQ-Quants as iqN_xx.\n");
printf(" Usually, attn-q-type can be one type below the chosen ffn type, and attn-v-type should be one type above.\n");
printf(" attn-qkv-type replaces the types attn-q, attn-k and attn-v on some models.\n");
//TODO: - eventually - harmonize the CAPS writing of the FTYPEs, and non CAPS writing of the GGML_TYPEs.
printf("\nAllowed quantization types:\n");
for (auto & it : QUANT_OPTIONS) {
if (it.name != "COPY") {
@@ -277,6 +293,54 @@ int main(int argc, char ** argv) {
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--attn-q-type") == 0) {
if (arg_idx < argc-1) {
params.attn_q_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--attn-k-type") == 0) {
if (arg_idx < argc-1) {
params.attn_k_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--attn-v-type") == 0) {
if (arg_idx < argc-1) {
params.attn_v_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--attn-qkv-type") == 0) {
if (arg_idx < argc-1) {
params.attn_qkv_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--attn-output-type") == 0) {
if (arg_idx < argc-1) {
params.attn_output_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--ffn-gate-type") == 0) {
if (arg_idx < argc-1) {
params.ffn_gate_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--ffn-down-type") == 0) {
if (arg_idx < argc-1) {
params.ffn_down_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--ffn-up-type") == 0) {
if (arg_idx < argc-1) {
params.ffn_up_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) {
usage(argv[0]);

8
include/llama.h
View File

@@ -361,6 +361,14 @@ extern "C" {
enum llama_ftype ftype; // quantize to this llama_ftype
enum ggml_type output_tensor_type; // output tensor type
enum ggml_type token_embedding_type; // token embeddings tensor type
enum ggml_type attn_q_type; // attention query tensor type
enum ggml_type attn_k_type; // attention key tensor type
enum ggml_type attn_v_type; // attention value tensor type
enum ggml_type attn_qkv_type; // attention query-key-value tensor type
enum ggml_type attn_output_type; // attention output tensor type
enum ggml_type ffn_gate_type; // feedforward network gate type
enum ggml_type ffn_down_type; // feedforward network down type
enum ggml_type ffn_up_type; // feedforward network up type
bool allow_requantize; // allow quantizing non-f32/f16 tensors
bool quantize_output_tensor; // quantize output.weight
bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored

56
src/llama.cpp
View File

@@ -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,