vbatts/llama.cpp
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Create a Custom Quantization Scheme (CQS) FTYPE

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And integrate it in the tensors quantization tree.
This commit is contained in:
Nexesenex 2024-08-09 16:53:23 +02:00
parent bd575f01de
commit 23198ce844
3 changed files with 45 additions and 14 deletions
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18
examples/quantize/quantize.cpp
View file

@ -52,6 +52,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "F16", LLAMA_FTYPE_MOSTLY_F16, "14.00G, +0.0020 ppl @ Mistral-7B", },
{ "BF16", LLAMA_FTYPE_MOSTLY_BF16, "14.00G, -0.0050 ppl @ Mistral-7B", },
{ "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", },
{ "CQS", LLAMA_FTYPE_CQS, "Custom Quantization Scheme", },
// Note: Ensure COPY comes after F32 to avoid ftype 0 from matching.
{ "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", },
};
@ -101,10 +102,10 @@ static void usage(const char * executable) {
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\n");
printf(" Optional specific tensor quantization types to amend the selected quantization strategy type:\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_embd.weight tensor.\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_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");
@ -118,10 +119,11 @@ static void usage(const char * executable) {
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");
printf("Note: Usually, attn-q-type can be one type below the chosen ffn type, and attn-v-type should be one type above.\n");
printf("Note: --attn-qkv-type replaces the types attn-q, attn-k, and attn-v on some models.\n");
printf("Note: Write the specific tensor legacy quants as qN_N, the K-Quants as qN_K, the IQ-Quants as iqN_xx.\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) {

1
include/llama.h
View file

@ -166,6 +166,7 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // except 1d tensors
LLAMA_FTYPE_CQS = 99, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};

40
src/llama.cpp
View file

@ -4478,6 +4478,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: return "Q4_0_4_4";
case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: return "Q4_0_4_8";
case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: return "Q4_0_8_8";
case LLAMA_FTYPE_CQS: return "Custom Quantization Scheme";
default: return "unknown, may not work";
}
@ -15381,7 +15382,10 @@ 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 (ftype == LLAMA_FTYPE_CQS && 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_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
@ -15419,7 +15423,10 @@ 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 (ftype == LLAMA_FTYPE_CQS && 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;
@ -15431,6 +15438,9 @@ 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_CQS && qs.params->attn_q_type < GGML_TYPE_COUNT) {
new_type = qs.params->attn_q_type;
}
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
new_type = GGML_TYPE_IQ3_XXS;
}
@ -15440,7 +15450,10 @@ 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 (ftype == LLAMA_FTYPE_CQS && 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;
}
@ -15483,7 +15496,10 @@ 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 (ftype == LLAMA_FTYPE_CQS && 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 ||
@ -15503,6 +15519,9 @@ 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_CQS && qs.params->attn_qkv_type < GGML_TYPE_COUNT) {
new_type = qs.params->attn_qkv_type;
}
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q4_K;
}
@ -15512,7 +15531,10 @@ 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 (ftype == LLAMA_FTYPE_CQS && 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;
}
++qs.i_ffn_gate;
@ -15520,7 +15542,10 @@ 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 (ftype == LLAMA_FTYPE_CQS && 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;
}
++qs.i_ffn_up;
@ -15671,6 +15696,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: default_type = GGML_TYPE_Q4_0_4_8; break;
case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: default_type = GGML_TYPE_Q4_0_8_8; break;
// Custom Quantization Scheme
case LLAMA_FTYPE_CQS: default_type = GGML_TYPE_Q2_K; break;
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
}