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Added support for iq1s and iq1m quantization type

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This commit is contained in:
leeetao
2025-04-17 10:27:53 +00:00
parent b212d74dc3 45ec52c2cb
commit fc1e2d3fc6
8 changed files with 973 additions and 523 deletions
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src/llama.cpp
+204 -99
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@@ -3560,16 +3560,26 @@ static bool is_dtype_exist(struct model_params * n_params, enum ggml_type dtype)
case GGML_TYPE_F32:
case GGML_TYPE_F16:
return true;
case GGML_TYPE_Q2_K:
return n_params->layer_q2k > 0 || n_params->output_q2k > 0;
case GGML_TYPE_Q4_K:
return n_params->layer_q4k > 0 || n_params->output_q4k > 0;
case GGML_TYPE_Q5_0:
return n_params->layer_q50 > 0 || n_params->output_q50 > 0;
return n_params->layer_q4k > 0 || n_params->output_q4k > 0;
case GGML_TYPE_Q5_K:
return n_params->layer_q5k > 0 || n_params->output_q5k > 0;
return n_params->layer_q5k > 0 || n_params->output_q5k > 0;
case GGML_TYPE_Q6_K:
return n_params->layer_q6k > 0 || n_params->output_q6k > 0;
return n_params->layer_q6k > 0 || n_params->output_q6k > 0;
case GGML_TYPE_IQ2_XXS:
return n_params->layer_iq2xxs > 0 || n_params->output_iq2xxs > 0;
case GGML_TYPE_Q5_0:
return n_params->layer_q50 > 0 || n_params->output_q50 > 0;
case GGML_TYPE_Q8_0:
return n_params->layer_q80 > 0 || n_params->output_q80 > 0;
return n_params->layer_q80 > 0 || n_params->output_q80 > 0;
case GGML_TYPE_IQ1_S:
return n_params->layer_iq1s > 0 || n_params->output_iq1s > 0;
case GGML_TYPE_IQ4_NL:
return n_params->layer_iq4nl > 0 || n_params->output_iq4nl > 0;
case GGML_TYPE_IQ1_M:
return n_params->layer_iq1m > 0 || n_params->output_iq1m > 0;
default:
throw std::runtime_error("Unrecognized data type\n");
}
@@ -3650,18 +3660,18 @@ void llama_profile_device(
dev_info->gpu_props.cuda_flops_f16_f32 = device_cuda_flops (model, GGML_TYPE_F16, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q2_K)) {
dev_info->cpu_props.flops_q2k_f32 = device_cpu_flops (model, GGML_TYPE_Q2_K, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q2k_f32 = device_metal_flops(model, GGML_TYPE_Q2_K, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q2k_f32 = device_cuda_flops (model, GGML_TYPE_Q2_K, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q4_K)) {
dev_info->cpu_props.flops_q4k_f32 = device_cpu_flops (model, GGML_TYPE_Q4_K, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q4k_f32 = device_metal_flops(model, GGML_TYPE_Q4_K, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q4k_f32 = device_cuda_flops (model, GGML_TYPE_Q4_K, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q5_0)) {
dev_info->cpu_props.flops_q50_f32 = device_cpu_flops (model, GGML_TYPE_Q5_0, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q50_f32 = device_metal_flops(model, GGML_TYPE_Q5_0, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q50_f32 = device_cuda_flops (model, GGML_TYPE_Q5_0, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q5_K)) {
dev_info->cpu_props.flops_q5k_f32 = device_cpu_flops (model, GGML_TYPE_Q5_K, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q5k_f32 = device_metal_flops(model, GGML_TYPE_Q5_K, GGML_TYPE_F32);
@@ -3674,11 +3684,42 @@ void llama_profile_device(
dev_info->gpu_props.cuda_flops_q6k_f32 = device_cuda_flops (model, GGML_TYPE_Q6_K, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_IQ2_XXS)) {
dev_info->cpu_props.flops_iq2xxs_f32 = device_cpu_flops (model, GGML_TYPE_IQ2_XXS, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_iq2xxs_f32= device_metal_flops(model, GGML_TYPE_IQ2_XXS, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_iq2xxs_f32 = device_cuda_flops (model, GGML_TYPE_IQ2_XXS, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q5_0)) {
dev_info->cpu_props.flops_q50_f32 = device_cpu_flops (model, GGML_TYPE_Q5_0, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q50_f32 = device_metal_flops(model, GGML_TYPE_Q5_0, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q50_f32 = device_cuda_flops (model, GGML_TYPE_Q5_0, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_Q8_0)) {
dev_info->cpu_props.flops_q80_f32 = device_cpu_flops (model, GGML_TYPE_Q8_0, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_q80_f32 = device_metal_flops(model, GGML_TYPE_Q8_0, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q80_f32 = device_cuda_flops (model, GGML_TYPE_Q8_0, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_IQ1_S)) {
dev_info->cpu_props.flops_iq1s_f32 = device_cpu_flops (model, GGML_TYPE_IQ1_S, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_iq1s_f32= device_metal_flops(model, GGML_TYPE_IQ1_S, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_iq1s_f32 = device_cuda_flops (model, GGML_TYPE_IQ1_S, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_IQ4_NL)) {
dev_info->cpu_props.flops_iq4nl_f32 = device_cpu_flops (model, GGML_TYPE_IQ4_NL, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_iq4nl_f32= device_metal_flops(model, GGML_TYPE_IQ4_NL, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_iq4nl_f32 = device_cuda_flops (model, GGML_TYPE_IQ4_NL, GGML_TYPE_F32);
}
if (is_dtype_exist(n_params, GGML_TYPE_IQ1_M)) {
dev_info->cpu_props.flops_iq1m_f32 = device_cpu_flops (model, GGML_TYPE_IQ1_M, GGML_TYPE_F32, n_threads);
dev_info->gpu_props.metal_flops_iq1m_f32= device_metal_flops(model, GGML_TYPE_IQ1_M, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_iq1m_f32 = device_cuda_flops (model, GGML_TYPE_IQ1_M, GGML_TYPE_F32);
}
}
ggml_backend_buffer_type_t llama_dev_buffer_type(struct llama_model * model, int device) {
@@ -4844,9 +4885,7 @@ struct llama_model_loader {
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
case GGML_TYPE_Q4_0_4_4: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_4; break;
case GGML_TYPE_Q4_0_4_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_8; break;
case GGML_TYPE_Q4_0_8_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_8_8; break;
default:
{
LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
@@ -5654,9 +5693,6 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw";
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";
default: return "unknown, may not work";
}
@@ -18997,10 +19033,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = GGML_TYPE_IQ3_S;
}
else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 ||
new_type == GGML_TYPE_Q4_0_8_8) {
new_type = GGML_TYPE_Q4_0;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_TQ1_0 || ftype == LLAMA_FTYPE_MOSTLY_TQ2_0) {
new_type = GGML_TYPE_Q4_K;
}
@@ -19323,10 +19355,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break;
case LLAMA_FTYPE_MOSTLY_IQ3_M: default_type = GGML_TYPE_IQ3_S; break;
case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: default_type = GGML_TYPE_Q4_0_4_4; break;
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;
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
}
@@ -19646,14 +19675,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
f32_data = (float *) f32_conv_buf.data();
}
int chunk_size_multiplier = 1;
if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 || new_type == GGML_TYPE_Q4_0_8_8) {
if ((new_type == GGML_TYPE_Q4_0_8_8) && (tensor->ne[1] % 8 != 0)) new_type = GGML_TYPE_Q4_0;
else if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0;
if (new_type == GGML_TYPE_Q4_0_8_8) chunk_size_multiplier = 8;
else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8) chunk_size_multiplier = 4;
}
LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
fflush(stdout);
@@ -19666,8 +19687,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
const int64_t nrows = tensor->ne[1];
static const int64_t min_chunk_size = 32 * 512;
const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row)) *
chunk_size_multiplier;
const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row));
const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;
@@ -21049,25 +21069,40 @@ static void count_n_flops(struct model_flops * n_flops, enum ggml_type dtype, en
case PROFILER_LAYER_OUTPUT:
switch (dtype) {
case GGML_TYPE_F32:
n_flops->output_f32_f32 += n;
n_flops->output_f32_f32 += n;
break;
case GGML_TYPE_F16:
n_flops->output_f16_f32 += n;
n_flops->output_f16_f32 += n;
break;
case GGML_TYPE_Q2_K:
n_flops->output_q2k_f32 += n;
break;
case GGML_TYPE_Q4_K:
n_flops->output_q4k_f32 += n;
break;
case GGML_TYPE_Q5_0:
n_flops->output_q50_f32 += n;
n_flops->output_q4k_f32 += n;
break;
case GGML_TYPE_Q5_K:
n_flops->output_q5k_f32 += n;
n_flops->output_q5k_f32 += n;
break;
case GGML_TYPE_Q6_K:
n_flops->output_q6k_f32 += n;
n_flops->output_q6k_f32 += n;
break;
case GGML_TYPE_IQ2_XXS:
n_flops->output_iq2xxs_f32 += n;
break;
case GGML_TYPE_Q5_0:
n_flops->output_q50_f32 += n;
break;
case GGML_TYPE_Q8_0:
n_flops->output_q80_f32 += n;
n_flops->output_q80_f32 += n;
break;
case GGML_TYPE_IQ1_S:
n_flops->output_iq1s_f32 += n;
break;
case GGML_TYPE_IQ4_NL:
n_flops->output_iq4nl_f32 += n;
break;
case GGML_TYPE_IQ1_M:
n_flops->output_iq1m_f32 += n;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_OUTPUT\n");
@@ -21075,27 +21110,42 @@ static void count_n_flops(struct model_flops * n_flops, enum ggml_type dtype, en
break;
case PROFILER_LAYER_BACKEND:
switch (dtype) {
switch (dtype) {
case GGML_TYPE_F32:
n_flops->layer_f32_f32 += n;
n_flops->layer_f32_f32 += n;
break;
case GGML_TYPE_F16:
n_flops->layer_f16_f32 += n;
n_flops->layer_f16_f32 += n;
break;
case GGML_TYPE_Q2_K:
n_flops->layer_q2k_f32 += n;
break;
case GGML_TYPE_Q4_K:
n_flops->layer_q4k_f32 += n;
break;
case GGML_TYPE_Q5_0:
n_flops->layer_q50_f32 += n;
n_flops->layer_q4k_f32 += n;
break;
case GGML_TYPE_Q5_K:
n_flops->layer_q5k_f32 += n;
n_flops->layer_q5k_f32 += n;
break;
case GGML_TYPE_Q6_K:
n_flops->layer_q6k_f32 += n;
n_flops->layer_q6k_f32 += n;
break;
case GGML_TYPE_IQ2_XXS:
n_flops->layer_iq2xxs_f32 += n;
break;
case GGML_TYPE_Q5_0:
n_flops->layer_q50_f32 += n;
break;
case GGML_TYPE_Q8_0:
n_flops->layer_q80_f32 += n;
n_flops->layer_q80_f32 += n;
break;
case GGML_TYPE_IQ1_S:
n_flops->layer_iq1s_f32 += n;
break;
case GGML_TYPE_IQ4_NL:
n_flops->layer_iq4nl_f32 += n;
break;
case GGML_TYPE_IQ1_M:
n_flops->layer_iq1m_f32 += n;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_BACKEND\n");
@@ -21113,25 +21163,40 @@ static void count_n_params(struct model_params * n_params, enum ggml_type dtype,
case PROFILER_LAYER_INPUT:
switch (dtype) {
case GGML_TYPE_F32:
n_params->input_f32 += n_i64t;
n_params->input_f32 += n_i64t;
break;
case GGML_TYPE_F16:
n_params->input_f16 += n_i64t;
n_params->input_f16 += n_i64t;
break;
case GGML_TYPE_Q2_K:
n_params->input_q2k += n_i64t;
break;
case GGML_TYPE_Q4_K:
n_params->input_q4k += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->input_q50 += n_i64t;
n_params->input_q4k += n_i64t;
break;
case GGML_TYPE_Q5_K:
n_params->input_q5k += n_i64t;
n_params->input_q5k += n_i64t;
break;
case GGML_TYPE_Q6_K:
n_params->input_q6k += n_i64t;
n_params->input_q6k += n_i64t;
break;
case GGML_TYPE_IQ2_XXS:
n_params->input_iq2xxs += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->input_q50 += n_i64t;
break;
case GGML_TYPE_Q8_0:
n_params->input_q80 += n_i64t;
n_params->input_q80 += n_i64t;
break;
case GGML_TYPE_IQ1_S:
n_params->input_iq1s += n_i64t;
break;
case GGML_TYPE_IQ4_NL:
n_params->input_iq4nl += n_i64t;
break;
case GGML_TYPE_IQ1_M:
n_params->input_iq1m += n_i64t;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_OUTPUT\n");
@@ -21141,25 +21206,40 @@ static void count_n_params(struct model_params * n_params, enum ggml_type dtype,
case PROFILER_LAYER_OUTPUT:
switch (dtype) {
case GGML_TYPE_F32:
n_params->output_f32 += n_i64t;
n_params->output_f32 += n_i64t;
break;
case GGML_TYPE_F16:
n_params->output_f16 += n_i64t;
n_params->output_f16 += n_i64t;
break;
case GGML_TYPE_Q2_K:
n_params->output_q2k += n_i64t;
break;
case GGML_TYPE_Q4_K:
n_params->output_q4k += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->output_q50 += n_i64t;
n_params->output_q4k += n_i64t;
break;
case GGML_TYPE_Q5_K:
n_params->output_q5k += n_i64t;
n_params->output_q5k += n_i64t;
break;
case GGML_TYPE_Q6_K:
n_params->output_q6k += n_i64t;
n_params->output_q6k += n_i64t;
break;
case GGML_TYPE_IQ2_XXS:
n_params->output_iq2xxs += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->output_q50 += n_i64t;
break;
case GGML_TYPE_Q8_0:
n_params->output_q80 += n_i64t;
n_params->output_q80 += n_i64t;
break;
case GGML_TYPE_IQ1_S:
n_params->output_iq1s += n_i64t;
break;
case GGML_TYPE_IQ4_NL:
n_params->output_iq4nl += n_i64t;
break;
case GGML_TYPE_IQ1_M:
n_params->output_iq1m += n_i64t;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_OUTPUT\n");
@@ -21169,25 +21249,40 @@ static void count_n_params(struct model_params * n_params, enum ggml_type dtype,
case PROFILER_LAYER_BACKEND:
switch (dtype) {
case GGML_TYPE_F32:
n_params->layer_f32 += n_i64t;
n_params->layer_f32 += n_i64t;
break;
case GGML_TYPE_F16:
n_params->layer_f16 += n_i64t;
n_params->layer_f16 += n_i64t;
break;
case GGML_TYPE_Q2_K:
n_params->layer_q2k += n_i64t;
break;
case GGML_TYPE_Q4_K:
n_params->layer_q4k += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->layer_q50 += n_i64t;
n_params->layer_q4k += n_i64t;
break;
case GGML_TYPE_Q5_K:
n_params->layer_q5k += n_i64t;
n_params->layer_q5k += n_i64t;
break;
case GGML_TYPE_Q6_K:
n_params->layer_q6k += n_i64t;
n_params->layer_q6k += n_i64t;
break;
case GGML_TYPE_IQ2_XXS:
n_params->layer_iq2xxs += n_i64t;
break;
case GGML_TYPE_Q5_0:
n_params->layer_q50 += n_i64t;
break;
case GGML_TYPE_Q8_0:
n_params->layer_q80 += n_i64t;
n_params->layer_q80 += n_i64t;
break;
case GGML_TYPE_IQ1_S:
n_params->layer_iq1s += n_i64t;
break;
case GGML_TYPE_IQ4_NL:
n_params->layer_iq4nl += n_i64t;
break;
case GGML_TYPE_IQ1_M:
n_params->layer_iq1m += n_i64t;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_BACKEND\n");
@@ -21477,23 +21572,33 @@ void llama_model_n_flops(
}
// use average values instead of total values
n_flops->layer_f32_f32 = static_cast<int64_t>((double)n_flops->layer_f32_f32 / (double)n_layer);
n_flops->layer_f16_f32 = static_cast<int64_t>((double)n_flops->layer_f16_f32 / (double)n_layer);
n_flops->layer_q4k_f32 = static_cast<int64_t>((double)n_flops->layer_q4k_f32 / (double)n_layer);
n_flops->layer_q50_f32 = static_cast<int64_t>((double)n_flops->layer_q50_f32 / (double)n_layer);
n_flops->layer_q5k_f32 = static_cast<int64_t>((double)n_flops->layer_q5k_f32 / (double)n_layer);
n_flops->layer_q6k_f32 = static_cast<int64_t>((double)n_flops->layer_q6k_f32 / (double)n_layer);
n_flops->layer_q80_f32 = static_cast<int64_t>((double)n_flops->layer_q80_f32 / (double)n_layer);
n_params->layer_f32 = static_cast<int64_t>((double)n_params->layer_f32 / (double)n_layer);
n_params->layer_f16 = static_cast<int64_t>((double)n_params->layer_f16 / (double)n_layer);
n_params->layer_q4k = static_cast<int64_t>((double)n_params->layer_q4k / (double)n_layer);
n_params->layer_q50 = static_cast<int64_t>((double)n_params->layer_q50 / (double)n_layer);
n_params->layer_q5k = static_cast<int64_t>((double)n_params->layer_q5k / (double)n_layer);
n_params->layer_q6k = static_cast<int64_t>((double)n_params->layer_q6k / (double)n_layer);
n_params->layer_q80 = static_cast<int64_t>((double)n_params->layer_q80 / (double)n_layer);
n_bytes->nb_layer = static_cast<int64_t>((double)n_bytes->nb_layer / (double)n_layer);
n_flops->layer_f32_f32 = static_cast<int64_t>((double)n_flops->layer_f32_f32 / (double)n_layer);
n_flops->layer_f16_f32 = static_cast<int64_t>((double)n_flops->layer_f16_f32 / (double)n_layer);
n_flops->layer_q2k_f32 = static_cast<int64_t>((double)n_flops->layer_q2k_f32 / (double)n_layer);
n_flops->layer_q4k_f32 = static_cast<int64_t>((double)n_flops->layer_q4k_f32 / (double)n_layer);
n_flops->layer_q5k_f32 = static_cast<int64_t>((double)n_flops->layer_q5k_f32 / (double)n_layer);
n_flops->layer_q6k_f32 = static_cast<int64_t>((double)n_flops->layer_q6k_f32 / (double)n_layer);
n_flops->layer_iq2xxs_f32 = static_cast<int64_t>((double)n_flops->layer_iq2xxs_f32 / (double)n_layer);
n_flops->layer_q50_f32 = static_cast<int64_t>((double)n_flops->layer_q50_f32 / (double)n_layer);
n_flops->layer_q80_f32 = static_cast<int64_t>((double)n_flops->layer_q80_f32 / (double)n_layer);
n_flops->layer_iq1s_f32 = static_cast<int64_t>((double)n_flops->layer_iq1s_f32 / (double)n_layer);
n_flops->layer_iq4nl_f32 = static_cast<int64_t>((double)n_flops->layer_iq4nl_f32 / (double)n_layer);
n_flops->layer_iq1m_f32 = static_cast<int64_t>((double)n_flops->layer_iq1m_f32 / (double)n_layer);
n_params->layer_f32 = static_cast<int64_t>((double)n_params->layer_f32 / (double)n_layer);
n_params->layer_f16 = static_cast<int64_t>((double)n_params->layer_f16 / (double)n_layer);
n_params->layer_q2k = static_cast<int64_t>((double)n_params->layer_q2k / (double)n_layer);
n_params->layer_q4k = static_cast<int64_t>((double)n_params->layer_q4k / (double)n_layer);
n_params->layer_q5k = static_cast<int64_t>((double)n_params->layer_q5k / (double)n_layer);
n_params->layer_q6k = static_cast<int64_t>((double)n_params->layer_q6k / (double)n_layer);
n_params->layer_iq2xxs = static_cast<int64_t>((double)n_params->layer_iq2xxs / (double)n_layer);
n_params->layer_q50 = static_cast<int64_t>((double)n_params->layer_q50 / (double)n_layer);
n_params->layer_q80 = static_cast<int64_t>((double)n_params->layer_q80 / (double)n_layer);
n_params->layer_iq1s = static_cast<int64_t>((double)n_params->layer_iq1s / (double)n_layer);
n_params->layer_iq4nl = static_cast<int64_t>((double)n_params->layer_iq4nl / (double)n_layer);
n_params->layer_iq1m = static_cast<int64_t>((double)n_params->layer_iq1m / (double)n_layer);
n_bytes->nb_layer = static_cast<int64_t>((double)n_bytes->nb_layer / (double)n_layer);
// reset ml, model, and clear contexts
ml->n_created = 0;