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Make sure tensor row size is multiple of block size also when quantizing with --pure #294

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Merged
merged 10 commits into from
Mar 27, 2025
Merged

Make sure tensor row size is multiple of block size also when quantizing with --pure #294

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970c164
WIP - not working
Mar 26, 2025
40ab112
q8_0 without bells and wistles works
Mar 26, 2025
8e2d549
It works for q8_0
Mar 26, 2025
9ce890e
Use bf16 instead of f16,int16
Mar 26, 2025
0170c8f
q4_0_r8
Mar 26, 2025
a4d7fb7
q5_0_r4
Mar 26, 2025
f1b4762
q6_0_r4
Mar 26, 2025
b428bf1
Also q4_1 and q5_1
Mar 26, 2025
75687d2
Add check if selected type is possible with --pure
Mar 27, 2025
7e706ab
Merge remote-tracking branch 'origin/main' into ik/change_q_pure
Mar 27, 2025
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167 changes: 83 additions & 84 deletions src/llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -16762,6 +16762,78 @@ static void llama_tensor_dequantize_internal(
workers.clear();
}

static ggml_type change_type_if_necessar(ggml_type new_type, int nx, int ny) {
bool convert_incompatible_tensor = false;
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
new_type == GGML_TYPE_IQ1_M || new_type == GGML_TYPE_IQ4_K || new_type == GGML_TYPE_IQ2_K ||
new_type == GGML_TYPE_IQ5_K || new_type == GGML_TYPE_IQ3_K || new_type == GGML_TYPE_Q4_K_R4 ||
new_type == GGML_TYPE_IQ6_K || new_type == GGML_TYPE_IQ4_KS || new_type == GGML_TYPE_IQ4_XS_R8 ||
new_type == GGML_TYPE_IQ2_KS || new_type == GGML_TYPE_IQ4_KSS || new_type == GGML_TYPE_Q6_K_R4 ||
new_type == GGML_TYPE_Q5_K_R4 || new_type == GGML_TYPE_Q3_K_R4 || new_type == GGML_TYPE_Q2_K_R4 ||
new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_K_R4||
new_type == GGML_TYPE_IQ2_K_R4|| new_type == GGML_TYPE_IQ5_K_R4|| new_type == GGML_TYPE_IQ4_KS_R4 ||
new_type == GGML_TYPE_IQ3_XXS_R4 || new_type == GGML_TYPE_IQ2_XXS_R4 || new_type == GGML_TYPE_IQ2_XS_R4 ||
new_type == GGML_TYPE_IQ2_S_R4|| new_type == GGML_TYPE_IQ3_S_R4) {
if (nx % QK_K != 0) {
LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
convert_incompatible_tensor = true;
}
}
if (new_type == GGML_TYPE_IQ1_BN || new_type == GGML_TYPE_IQ2_BN || new_type == GGML_TYPE_IQ2_BN_R4) {
if (nx % QK_IQ1BN != 0) {
convert_incompatible_tensor = true;
}
}
if (convert_incompatible_tensor) {
switch (new_type) {
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XXS_R4:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_XS_R4:
case GGML_TYPE_IQ2_KS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ3_XXS_R4:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ3_S_R4:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q2_K_R4:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q3_K_R4:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ2_K_R4:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ3_K_R4:
case GGML_TYPE_IQ4_KSS:
case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ4_KS_R4:
case GGML_TYPE_IQ4_XS_R8:
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ4_K_R4:
case GGML_TYPE_Q4_K_R4:
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
case GGML_TYPE_IQ5_K:
case GGML_TYPE_IQ5_K_R4:
case GGML_TYPE_Q5_K_R4:
case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q6_0; break;
case GGML_TYPE_IQ6_K:
case GGML_TYPE_Q6_K_R4:
case GGML_TYPE_Q8_K_R8:
case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
}
LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
}
return new_type;
}

static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
const std::string name = ggml_get_name(tensor);

Expand Down Expand Up @@ -17260,90 +17332,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
LLAMA_LOG_INFO("Using custom type %s for tensor %s\n", ggml_type_name(new_type), name.c_str());
}

// if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
//}
// IK: let's remove this, else Q2_K is almost the same as Q3_K_S
//else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) {
// if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
//}
// This can be used to reduce the size of the Q5_K_S model.
// The associated PPL increase is fully in line with the size reduction
//else {
// if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
//}
bool convert_incompatible_tensor = false;
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
new_type == GGML_TYPE_IQ1_M || new_type == GGML_TYPE_IQ4_K || new_type == GGML_TYPE_IQ2_K ||
new_type == GGML_TYPE_IQ5_K || new_type == GGML_TYPE_IQ3_K || new_type == GGML_TYPE_Q4_K_R4 ||
new_type == GGML_TYPE_IQ6_K || new_type == GGML_TYPE_IQ4_KS || new_type == GGML_TYPE_IQ4_XS_R8 ||
new_type == GGML_TYPE_IQ2_KS || new_type == GGML_TYPE_IQ4_KSS || new_type == GGML_TYPE_Q6_K_R4 ||
new_type == GGML_TYPE_Q5_K_R4 || new_type == GGML_TYPE_Q3_K_R4 || new_type == GGML_TYPE_Q2_K_R4 ||
new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_K_R4||
new_type == GGML_TYPE_IQ2_K_R4|| new_type == GGML_TYPE_IQ5_K_R4|| new_type == GGML_TYPE_IQ4_KS_R4 ||
new_type == GGML_TYPE_IQ3_XXS_R4 || new_type == GGML_TYPE_IQ2_XXS_R4 || new_type == GGML_TYPE_IQ2_XS_R4 ||
new_type == GGML_TYPE_IQ2_S_R4|| new_type == GGML_TYPE_IQ3_S_R4) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K != 0) {
LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
convert_incompatible_tensor = true;
} else {
++qs.n_k_quantized;
}
}
if (new_type == GGML_TYPE_IQ1_BN || new_type == GGML_TYPE_IQ2_BN || new_type == GGML_TYPE_IQ2_BN_R4) {
int nx = tensor->ne[0];
if (nx % QK_IQ1BN != 0) {
convert_incompatible_tensor = true;
}
}
if (convert_incompatible_tensor) {
switch (new_type) {
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XXS_R4:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_XS_R4:
case GGML_TYPE_IQ2_KS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_S_R4:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ3_XXS_R4:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ3_S_R4:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q2_K_R4:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q3_K_R4:
case GGML_TYPE_IQ2_K:
case GGML_TYPE_IQ2_K_R4:
case GGML_TYPE_IQ3_K:
case GGML_TYPE_IQ3_K_R4:
case GGML_TYPE_IQ4_KSS:
case GGML_TYPE_IQ4_KS:
case GGML_TYPE_IQ4_KS_R4:
case GGML_TYPE_IQ4_XS_R8:
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
case GGML_TYPE_IQ4_K:
case GGML_TYPE_IQ4_K_R4:
case GGML_TYPE_Q4_K_R4:
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
case GGML_TYPE_IQ5_K:
case GGML_TYPE_IQ5_K_R4:
case GGML_TYPE_Q5_K_R4:
case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q6_0; break;
case GGML_TYPE_IQ6_K:
case GGML_TYPE_Q6_K_R4:
case GGML_TYPE_Q8_K_R8:
case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
}
LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
auto working_type = change_type_if_necessar(new_type, tensor->ne[0], tensor->ne[1]);
if (working_type != new_type) {
++qs.n_fallback;
new_type = working_type;
}

return new_type;
Expand Down Expand Up @@ -17848,7 +17840,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}

// get more optimal quantization type based on the tensor shape, layer, etc.
if (!params->pure && ggml_is_quantized(default_type)) {
if (params->pure) {
auto working_type = change_type_if_necessar(new_type, tensor->ne[0], tensor->ne[1]);
if (working_type != new_type) {
++qs.n_fallback;
new_type = working_type;
}
}
else if (ggml_is_quantized(default_type)) {
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
}
if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
Expand Down