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quantize: be able to specify the token embedding tensor type
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7883796f71
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0e826d12a5
@ -221,6 +221,12 @@ int main(int argc, char ** argv) {
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} else {
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} else {
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usage(argv[0]);
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usage(argv[0]);
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}
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}
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} else if (strcmp(argv[arg_idx], "--token-embedding-type") == 0) {
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if (arg_idx < argc-1) {
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params.token_embedding_type = parse_ggml_type(argv[++arg_idx]);
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} else {
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usage(argv[0]);
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}
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} else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
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} else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
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params.allow_requantize = true;
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params.allow_requantize = true;
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} else if (strcmp(argv[arg_idx], "--pure") == 0) {
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} else if (strcmp(argv[arg_idx], "--pure") == 0) {
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22
llama.cpp
22
llama.cpp
@ -11987,15 +11987,18 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
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}
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}
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}
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}
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} else if (name == "token_embd.weight") {
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} else if (name == "token_embd.weight") {
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if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
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if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
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ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
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new_type = qs.params->token_embedding_type;
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new_type = GGML_TYPE_Q2_K;
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} else {
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}
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if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
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else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
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new_type = GGML_TYPE_Q2_K;
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new_type = GGML_TYPE_IQ3_S;
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}
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}
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else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
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else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
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new_type = GGML_TYPE_IQ3_S;
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new_type = GGML_TYPE_IQ3_S;
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}
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else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
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new_type = GGML_TYPE_IQ3_S;
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}
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}
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}
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} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
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} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
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ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
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ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
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@ -12892,6 +12895,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
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/*.nthread =*/ 0,
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/*.nthread =*/ 0,
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/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
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/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
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/*.output_tensor_type =*/ GGML_TYPE_COUNT,
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/*.output_tensor_type =*/ GGML_TYPE_COUNT,
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/*.token_embedding_type =*/ GGML_TYPE_COUNT,
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/*.allow_requantize =*/ false,
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/*.allow_requantize =*/ false,
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/*.quantize_output_tensor =*/ true,
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/*.quantize_output_tensor =*/ true,
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/*.only_copy =*/ false,
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/*.only_copy =*/ false,
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17
llama.h
17
llama.h
@ -275,14 +275,15 @@ extern "C" {
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// model quantization parameters
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// model quantization parameters
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typedef struct llama_model_quantize_params {
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typedef struct llama_model_quantize_params {
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int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
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int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
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enum llama_ftype ftype; // quantize to this llama_ftype
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enum llama_ftype ftype; // quantize to this llama_ftype
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enum ggml_type output_tensor_type; // output tensor type
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enum ggml_type output_tensor_type; // output tensor type
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bool allow_requantize; // allow quantizing non-f32/f16 tensors
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enum ggml_type token_embedding_type; // itoken embeddings tensor type
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bool quantize_output_tensor; // quantize output.weight
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bool allow_requantize; // allow quantizing non-f32/f16 tensors
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bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
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bool quantize_output_tensor; // quantize output.weight
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bool pure; // quantize all tensors to the default type
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bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
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void * imatrix; // pointer to importance matrix data
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bool pure; // quantize all tensors to the default type
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void * imatrix; // pointer to importance matrix data
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} llama_model_quantize_params;
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} llama_model_quantize_params;
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// grammar types
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// grammar types
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