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* hexstate_quantize.c β HExState GGUF Quantizer
*
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* β HPC-Optimized GGUF Quantization Engine β
* β β
* β Architecture: HPCGraph Sensitivity Propagation β
* β Optimization: Complex Amplitude BP + MCMC Scale Search β
* β Enhancements: MSE Grid Search, Importance Matrix Weighting β
* β Output: GGUF v3 (Q2_K) β
* β β
* β "The weight and the quantized are opposite faces." β
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
*
* This tool adapts the HExState HPC Ouroboros factoring engine for
* LLM weight quantization. The core mathematical machinery is reused:
*
* Factoring Domain β Quantization Domain
* βββββββββββββββββββββββββββββββββββββββββββββββββ
* HPCGraph + CZ edges β Block sensitivity graph
* Complex Amplitude BP β Importance propagation
* MCMC period sampler β Optimal scale search
* try_period() validation β Error bound checking
* LLL lattice reduction β (future) Adaptive bit allocation
*
* Additional techniques ported from llm-compressor:
* MSE grid search β Optimal min/max range shrinking
* Importance matrix (imatrix) β Per-channel error weighting
*
* Build:
* make -f Makefile.quantize
*
* Usage:
* ./hexstate_quantize <input> <output.gguf> [options]
*
* Input can be:
* - A single .safetensors file
* - A model directory containing sharded .safetensors files
*
* Options:
* --optimizer hpc|mse|hybrid Scale optimization strategy (default: hybrid)
* --imatrix <file> Importance matrix for weighted quantization
* --verbose Per-block diagnostics
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
#include <stdio.h>
#ifdef _OPENMP
#include <omp.h>
#endif
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <time.h>
#include <sys/stat.h>
#include <mpfr.h>
/* HExState headers β reused from the factoring engine */
#include "quhit_triality.h"
#include "hpc_graph.h"
#include "hpc_mobius.h"
#include "s6_exotic.h"
/* Quantization-specific headers */
#include "gguf_format.h"
#include "safetensors_reader.h"
#include "tokenizer_reader.h"
#include "imatrix_reader.h"
#define D 6 /* Preserved from HExState β the triality dimension */
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* OPTIMIZER MODE
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
typedef enum {
OPT_HPC, /* HExState BP only */
OPT_MSE, /* MSE grid search only */
OPT_HYBRID /* HPC sensitivity + MSE */
} OptimizerMode;
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* MODEL ARCHITECTURE AUTO-DETECTION
*
* Infers model architecture metadata from tensor names and shapes.
* Supports: LLaMA, Mistral, Qwen2, Phi-3, Gemma, GPT-NeoX, Falcon, DeepSeek
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
typedef struct {
char architecture[64]; /* "llama", "phi3", "gemma", etc. */
char name[256]; /* Human-readable model name */
uint32_t block_count; /* Number of transformer layers */
uint32_t embedding_length; /* Hidden dimension */
uint32_t head_count; /* Number of attention heads */
uint32_t head_count_kv; /* Number of KV heads (GQA) */
uint32_t vocab_size; /* Vocabulary size */
uint32_t context_length; /* Max context length (default) */
float rope_freq_base; /* RoPE frequency base */
uint32_t feed_forward_length; /* FFN intermediate size */
float rms_norm_eps; /* RMS norm epsilon */
int has_bias; /* Whether attention has biases */
int tie_word_embeddings; /* Whether output = embed_tokens */
} ModelArchitecture;
/* Count tensor names matching a pattern prefix */
static int count_tensors_with_prefix(const STMultiFile *mf, const char *prefix)
{
int count = 0;
int prefix_len = strlen(prefix);
for (int i = 0; i < mf->n_tensors; i++) {
if (strncmp(mf->tensor_map[i].name, prefix, prefix_len) == 0)
count++;
}
return count;
}
/* Find max layer index from tensor names like "model.layers.N.xxx" */
static int find_max_layer_index(const STMultiFile *mf, const char *layer_prefix)
{
int max_idx = -1;
int prefix_len = strlen(layer_prefix);
for (int i = 0; i < mf->n_tensors; i++) {
if (strncmp(mf->tensor_map[i].name, layer_prefix, prefix_len) == 0) {
int idx = atoi(mf->tensor_map[i].name + prefix_len);
if (idx > max_idx) max_idx = idx;
}
}
return max_idx;
}
/* ββ Config.json reader for definitive architecture parameters ββ */
typedef struct {
int valid;
uint32_t hidden_size;
uint32_t intermediate_size;
uint32_t num_attention_heads;
uint32_t num_key_value_heads;
uint32_t num_hidden_layers;
uint32_t vocab_size;
uint32_t max_position_embeddings;
float rope_theta;
float rms_norm_eps;
char model_type[64];
int tie_word_embeddings;
} ConfigJson;
static ConfigJson parse_config_json(const char *path)
{
ConfigJson cfg;
memset(&cfg, 0, sizeof(cfg));
FILE *f = fopen(path, "rb");
if (!f) return cfg;
fseek(f, 0, SEEK_END);
long size = ftell(f);
fseek(f, 0, SEEK_SET);
char *json = (char *)malloc(size + 1);
if (!json) { fclose(f); return cfg; }
fread(json, 1, size, f);
json[size] = '\0';
fclose(f);
cfg.valid = 1;
/* Simple key-value extraction */
const char *p;
p = tok_find_key(json, "hidden_size");
if (p) cfg.hidden_size = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(json, "intermediate_size");
if (p) cfg.intermediate_size = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(json, "num_attention_heads");
if (p) cfg.num_attention_heads = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(json, "num_key_value_heads");
if (p) cfg.num_key_value_heads = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(json, "num_hidden_layers");
if (p) cfg.num_hidden_layers = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(json, "vocab_size");
if (p) cfg.vocab_size = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(json, "max_position_embeddings");
if (p) cfg.max_position_embeddings = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(json, "rope_theta");
if (p) cfg.rope_theta = (float)strtod(p, NULL);
p = tok_find_key(json, "rms_norm_eps");
if (p) cfg.rms_norm_eps = (float)strtod(p, NULL);
p = tok_find_key(json, "model_type");
if (p && *p == '"') {
char buf[64];
tok_extract_string(p, buf, sizeof(buf));
strncpy(cfg.model_type, buf, sizeof(cfg.model_type) - 1);
}
p = tok_find_key(json, "tie_word_embeddings");
if (p) cfg.tie_word_embeddings = (strncmp(p, "true", 4) == 0);
/* ββ Qwen 3.5/3.6: parameters are nested inside "text_config" ββ */
if (cfg.hidden_size == 0) {
const char *tc = strstr(json, "\"text_config\"");
if (tc) {
const char *tc_brace = strchr(tc, '{');
if (tc_brace) {
p = tok_find_key(tc_brace, "hidden_size");
if (p) cfg.hidden_size = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(tc_brace, "intermediate_size");
if (p) cfg.intermediate_size = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(tc_brace, "num_attention_heads");
if (p) cfg.num_attention_heads = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(tc_brace, "num_key_value_heads");
if (p) cfg.num_key_value_heads = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(tc_brace, "num_hidden_layers");
if (p) cfg.num_hidden_layers = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(tc_brace, "vocab_size");
if (p) cfg.vocab_size = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(tc_brace, "max_position_embeddings");
if (p) cfg.max_position_embeddings = (uint32_t)strtol(p, NULL, 10);
p = tok_find_key(tc_brace, "rms_norm_eps");
if (p) cfg.rms_norm_eps = (float)strtod(p, NULL);
p = tok_find_key(tc_brace, "model_type");
if (p && *p == '"') {
char buf2[64];
tok_extract_string(p, buf2, sizeof(buf2));
strncpy(cfg.model_type, buf2, sizeof(cfg.model_type) - 1);
}
p = tok_find_key(tc_brace, "tie_word_embeddings");
if (p) cfg.tie_word_embeddings = (strncmp(p, "true", 4) == 0);
/* Qwen3.6 rope_theta is nested in rope_parameters */
const char *rp = strstr(tc_brace, "\"rope_parameters\"");
if (rp) {
p = tok_find_key(rp, "rope_theta");
if (p) cfg.rope_theta = (float)strtod(p, NULL);
}
}
}
}
free(json);
return cfg;
}
static void detect_architecture(const STMultiFile *mf, ModelArchitecture *arch,
const char *config_json_path)
{
memset(arch, 0, sizeof(*arch));
/* Default values */
strcpy(arch->architecture, "llama");
strcpy(arch->name, "HExState-quantized");
arch->context_length = 4096;
arch->rope_freq_base = 10000.0f;
arch->rms_norm_eps = 1e-5f;
/* ββ Try config.json for definitive parameters ββ */
ConfigJson cfg = {0};
if (config_json_path) {
cfg = parse_config_json(config_json_path);
}
if (cfg.valid) {
/* Map model_type to GGUF architecture name */
if (strcmp(cfg.model_type, "llama") == 0 ||
strcmp(cfg.model_type, "mistral") == 0) {
strcpy(arch->architecture, "llama");
} else if (strcmp(cfg.model_type, "qwen2") == 0) {
strcpy(arch->architecture, "qwen2");
} else if (strcmp(cfg.model_type, "qwen2_moe") == 0) {
strcpy(arch->architecture, "qwen2moe");
} else if (strcmp(cfg.model_type, "qwen3_5") == 0 ||
strcmp(cfg.model_type, "qwen3_5_text") == 0 ||
strcmp(cfg.model_type, "qwen3_5_moe") == 0) {
strcpy(arch->architecture, "qwen2"); /* GGUF arch: qwen2 compat */
} else if (strcmp(cfg.model_type, "phi3") == 0 ||
strcmp(cfg.model_type, "phi") == 0) {
strcpy(arch->architecture, "phi3");
} else if (strcmp(cfg.model_type, "gemma") == 0 ||
strcmp(cfg.model_type, "gemma2") == 0) {
strcpy(arch->architecture, "gemma");
} else if (strcmp(cfg.model_type, "deepseek_v2") == 0) {
strcpy(arch->architecture, "llama");
} else if (strcmp(cfg.model_type, "gpt_neox") == 0) {
strcpy(arch->architecture, "gpt_neox");
} else if (strcmp(cfg.model_type, "falcon") == 0) {
strcpy(arch->architecture, "falcon");
} else if (cfg.model_type[0]) {
/* Unknown β try llama as fallback */
strcpy(arch->architecture, "llama");
}
if (cfg.hidden_size) arch->embedding_length = cfg.hidden_size;
if (cfg.intermediate_size) arch->feed_forward_length = cfg.intermediate_size;
if (cfg.num_attention_heads) arch->head_count = cfg.num_attention_heads;
if (cfg.num_key_value_heads) arch->head_count_kv = cfg.num_key_value_heads;
if (cfg.num_hidden_layers) arch->block_count = cfg.num_hidden_layers;
if (cfg.vocab_size) arch->vocab_size = cfg.vocab_size;
if (cfg.max_position_embeddings) arch->context_length = cfg.max_position_embeddings;
if (cfg.rope_theta > 0) arch->rope_freq_base = cfg.rope_theta;
if (cfg.rms_norm_eps > 0) arch->rms_norm_eps = cfg.rms_norm_eps;
arch->tie_word_embeddings = cfg.tie_word_embeddings;
printf(" Architecture determined from config.json: %s\n", cfg.model_type);
}
/* ββ Fall back to tensor name pattern detection ββ */
int has_model_layers = count_tensors_with_prefix(mf, "model.layers.");
int has_gpt_neox = count_tensors_with_prefix(mf, "gpt_neox.");
int has_transformer = count_tensors_with_prefix(mf, "transformer.");
/* Architecture-specific detection */
int has_qkv_proj = count_tensors_with_prefix(mf, "model.layers.0.self_attn.qkv_proj");
int has_kv_a_proj = count_tensors_with_prefix(mf, "model.layers.0.self_attn.kv_a_proj_with_mqa");
int has_final_norm = (st_multi_find_tensor(mf, "model.final_norm.weight") >= 0);
if (has_qkv_proj > 0 && !cfg.valid) {
strcpy(arch->architecture, "phi3");
} else if (has_kv_a_proj > 0 && !cfg.valid) {
strcpy(arch->architecture, "llama"); /* DeepSeek uses llama arch */
} else if (has_final_norm && !cfg.valid) {
strcpy(arch->architecture, "gemma");
}
if (has_model_layers > 0 && arch->block_count == 0) {
arch->block_count = find_max_layer_index(mf, "model.layers.") + 1;
}
/* Infer dimensions from tensor shapes if not from config.json */
if (arch->embedding_length == 0 || arch->head_count == 0) {
int qproj_idx = st_multi_find_tensor(mf, "model.layers.0.self_attn.q_proj.weight");
int kproj_idx = st_multi_find_tensor(mf, "model.layers.0.self_attn.k_proj.weight");
if (qproj_idx >= 0) {
const STTensorInfo *ti = st_multi_tensor_info(mf, qproj_idx);
int64_t q_out = ti->shape[0];
int64_t hidden = ti->shape[1];
if (arch->embedding_length == 0) arch->embedding_length = hidden;
/* Try common head dimensions: 128, 64, 96 */
int head_dim = 128;
if (q_out % 128 == 0) head_dim = 128;
else if (q_out % 96 == 0) head_dim = 96;
else if (q_out % 64 == 0) head_dim = 64;
if (arch->head_count == 0) arch->head_count = q_out / head_dim;
if (kproj_idx >= 0 && arch->head_count_kv == 0) {
const STTensorInfo *kt = st_multi_tensor_info(mf, kproj_idx);
arch->head_count_kv = kt->shape[0] / head_dim;
}
}
}
if (arch->vocab_size == 0) {
int embed_idx = st_multi_find_tensor(mf, "model.embed_tokens.weight");
if (embed_idx >= 0) {
const STTensorInfo *ti = st_multi_tensor_info(mf, embed_idx);
arch->vocab_size = ti->shape[0];
}
}
if (arch->feed_forward_length == 0) {
int gate_idx = st_multi_find_tensor(mf, "model.layers.0.mlp.gate_proj.weight");
if (gate_idx >= 0) {
const STTensorInfo *ti = st_multi_tensor_info(mf, gate_idx);
arch->feed_forward_length = ti->shape[0];
} else {
int up_idx = st_multi_find_tensor(mf, "model.layers.0.mlp.up_proj.weight");
if (up_idx >= 0) {
const STTensorInfo *ti = st_multi_tensor_info(mf, up_idx);
arch->feed_forward_length = ti->shape[0];
}
}
}
/* Check for attention bias */
arch->has_bias = (st_multi_find_tensor(mf, "model.layers.0.self_attn.q_proj.bias") >= 0);
if (has_gpt_neox > 0 && arch->block_count == 0) {
strcpy(arch->architecture, "gpt_neox");
arch->block_count = find_max_layer_index(mf, "gpt_neox.layers.") + 1;
}
if (has_transformer > 0 && arch->block_count == 0) {
strcpy(arch->architecture, "falcon");
arch->block_count = find_max_layer_index(mf, "transformer.h.") + 1;
}
/* Fill in defaults for anything we couldn't detect */
if (arch->head_count == 0) arch->head_count = 32;
if (arch->head_count_kv == 0) arch->head_count_kv = arch->head_count;
if (arch->embedding_length == 0) arch->embedding_length = 4096;
if (arch->vocab_size == 0) arch->vocab_size = 32000;
if (arch->feed_forward_length == 0)
arch->feed_forward_length = (arch->embedding_length * 8) / 3; /* SwiGLU default */
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* TENSOR NAME MAPPING: HuggingFace β GGUF Standard
*
* Maps SafeTensors tensor names to the standardized GGUF naming
* convention used by llama.cpp for model loading.
*
* Enhanced with mappings for Phi-3, Gemma, DeepSeek, MoE, and bias tensors.
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
/* Returns 1 if this tensor should be skipped (not written to GGUF) */
static int should_skip_tensor(const char *hf_name)
{
/* Rotary embeddings are computed at runtime, not stored */
if (strstr(hf_name, "rotary_emb.inv_freq") != NULL) return 1;
if (strstr(hf_name, "rotary_emb.cos_cached") != NULL) return 1;
if (strstr(hf_name, "rotary_emb.sin_cached") != NULL) return 1;
/* Qwen 3.6 vision encoder β skip all visual.* tensors */
if (strncmp(hf_name, "model.visual.", 13) == 0) return 1;
if (strncmp(hf_name, "visual.", 7) == 0) return 1;
/* MTP (multi-token prediction) layers β not needed for inference */
if (strstr(hf_name, "model.language_model.mtp_") != NULL) return 1;
return 0;
}
static void map_tensor_name(const char *hf_name, char *gguf_name, int buflen)
{
/* Start with identity mapping */
strncpy(gguf_name, hf_name, buflen - 1);
gguf_name[buflen - 1] = '\0';
/* Top-level mappings (common to all architectures) */
struct { const char *from; const char *to; } mappings[] = {
{"model.embed_tokens.weight", "token_embd.weight"},
{"model.language_model.embed_tokens.weight","token_embd.weight"}, /* Qwen 3.6 */
{"model.norm.weight", "output_norm.weight"},
{"model.language_model.norm.weight", "output_norm.weight"}, /* Qwen 3.6 */
{"model.final_norm.weight", "output_norm.weight"}, /* Gemma */
{"lm_head.weight", "output.weight"},
{"model.embed_tokens.bias", "token_embd.bias"},
{"model.norm.bias", "output_norm.bias"},
{NULL, NULL}
};
for (int m = 0; mappings[m].from; m++) {
if (strcmp(hf_name, mappings[m].from) == 0) {
strncpy(gguf_name, mappings[m].to, buflen - 1);
return;
}
}
/* Layer mappings: "model.layers.N.xxx" or "model.language_model.layers.N.xxx" β "blk.N.xxx" */
const char *layer_prefix = NULL;
if (strncmp(hf_name, "model.layers.", 13) == 0)
layer_prefix = hf_name + 13;
else if (strncmp(hf_name, "model.language_model.layers.", 27) == 0)
layer_prefix = hf_name + 27;
if (layer_prefix) {
int layer_idx;
char rest[ST_MAX_NAME_LEN];
if (sscanf(layer_prefix, "%d.%255s", &layer_idx, rest) == 2) {
/* Map sublayer names */
struct { const char *from; const char *to; } layer_maps[] = {
/* Standard attention projections */
{"self_attn.q_proj.weight", "attn_q.weight"},
{"self_attn.k_proj.weight", "attn_k.weight"},
{"self_attn.v_proj.weight", "attn_v.weight"},
{"self_attn.o_proj.weight", "attn_output.weight"},
/* Attention biases */
{"self_attn.q_proj.bias", "attn_q.bias"},
{"self_attn.k_proj.bias", "attn_k.bias"},
{"self_attn.v_proj.bias", "attn_v.bias"},
{"self_attn.o_proj.bias", "attn_output.bias"},
/* Phi-3 fused QKV */
{"self_attn.qkv_proj.weight", "attn_qkv.weight"},
{"self_attn.qkv_proj.bias", "attn_qkv.bias"},
/* DeepSeek MLA */
{"self_attn.kv_a_proj_with_mqa.weight", "attn_kv_a_mqa.weight"},
{"self_attn.kv_b_proj.weight", "attn_kv_b.weight"},
/* Standard FFN (SwiGLU) */
{"mlp.gate_proj.weight", "ffn_gate.weight"},
{"mlp.up_proj.weight", "ffn_up.weight"},
{"mlp.down_proj.weight", "ffn_down.weight"},
/* FFN biases */
{"mlp.gate_proj.bias", "ffn_gate.bias"},
{"mlp.up_proj.bias", "ffn_up.bias"},
{"mlp.down_proj.bias", "ffn_down.bias"},
/* MoE gate */
{"mlp.gate.weight", "ffn_gate_inp.weight"},
/* MoE expert weights */
{"mlp.experts.gate_proj.weight", "ffn_gate_exps.weight"},
{"mlp.experts.up_proj.weight", "ffn_up_exps.weight"},
{"mlp.experts.down_proj.weight", "ffn_down_exps.weight"},
/* Norm layers */
{"input_layernorm.weight", "attn_norm.weight"},
{"post_attention_layernorm.weight", "ffn_norm.weight"},
{"input_layernorm.bias", "attn_norm.bias"},
{"post_attention_layernorm.bias", "ffn_norm.bias"},
/* Gemma pre/post feedforward norm */
{"pre_feedforward_layernorm.weight", "ffn_norm.weight"},
{"post_feedforward_layernorm.weight", "ffn_post_norm.weight"},
/* Qwen 3.6 full attention QK norms */
{"self_attn.q_norm.weight", "attn_q_norm.weight"},
{"self_attn.k_norm.weight", "attn_k_norm.weight"},
/* Qwen 3.6 DeltaNet (Gated Linear Attention) */
{"linear_attn.in_proj_qkv.weight", "ssm_in_qkv.weight"},
{"linear_attn.in_proj_z.weight", "ssm_in_z.weight"},
{"linear_attn.in_proj_a.weight", "ssm_in_a.weight"},
{"linear_attn.in_proj_b.weight", "ssm_in_b.weight"},
{"linear_attn.out_proj.weight", "ssm_out.weight"},
{"linear_attn.conv1d.weight", "ssm_conv1d.weight"},
{"linear_attn.norm.weight", "ssm_norm.weight"},
{"linear_attn.A_log", "ssm_a"},
{"linear_attn.dt_bias", "ssm_dt.bias"},
{NULL, NULL}
};
for (int m = 0; layer_maps[m].from; m++) {
if (strcmp(rest, layer_maps[m].from) == 0) {
snprintf(gguf_name, buflen, "blk.%d.%s",
layer_idx, layer_maps[m].to);
return;
}
}
/* MoE expert layer mapping: model.layers.N.mlp.experts.E.xxx */
int expert_idx;
char expert_rest[ST_MAX_NAME_LEN];
if (sscanf(rest, "mlp.experts.%d.%255s", &expert_idx, expert_rest) == 2) {
struct { const char *from; const char *to; } expert_maps[] = {
{"gate_proj.weight", "ffn_gate_exp.weight"},
{"up_proj.weight", "ffn_up_exp.weight"},
{"down_proj.weight", "ffn_down_exp.weight"},
{NULL, NULL}
};
for (int m = 0; expert_maps[m].from; m++) {
if (strcmp(expert_rest, expert_maps[m].from) == 0) {
snprintf(gguf_name, buflen, "blk.%d.%s.%d",
layer_idx, expert_maps[m].to, expert_idx);
return;
}
}
}
/* Fallback: keep original sub-path */
snprintf(gguf_name, buflen, "blk.%d.%s", layer_idx, rest);
}
}
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* SHOULD THIS TENSOR BE QUANTIZED?
*
* Decision rules:
* - Quantize: weight matrices (2D, large)
* - Keep F32: norms, biases, embeddings, 1D tensors
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
static int should_quantize(const STTensorInfo *ti, const char *gguf_name)
{
/* Never quantize 1D tensors (norms, biases) */
if (ti->n_dims < 2) return 0;
/* Never quantize embedding tables (row dimension = vocab) */
if (strstr(gguf_name, "token_embd") != NULL) return 0;
/* Never quantize LM head output β use exact match, not substring,
* to avoid matching "attn_output.weight" */
if (strcmp(gguf_name, "output.weight") == 0) return 0;
/* Never quantize norm weights */
if (strstr(gguf_name, "norm") != NULL) return 0;
/* Never quantize bias tensors */
if (strstr(gguf_name, ".bias") != NULL) return 0;
/* Never quantize MoE gate routing weights */
if (strstr(gguf_name, "ffn_gate_inp") != NULL) return 0;
/* Never quantize DeltaNet state-space parameters (1D or small) */
if (strstr(gguf_name, "ssm_a") != NULL) return 0; /* A_log */
if (strstr(gguf_name, "ssm_dt") != NULL) return 0; /* dt_bias */
if (strstr(gguf_name, "ssm_conv1d") != NULL) return 0; /* conv kernel */
/* Quantize everything else (attention projections, FFN weights, SSM projections) */
return 1;
}
/* Detect attention Q/K/V/O projection tensors.
* These are the most sensitive to quantization β errors in attention scores
* cascade through the entire sequence, causing self-correction loops.
* Promoting these to Q4_0 (~4.5bpw) doubles their precision. */
static int is_attention_tensor(const char *gguf_name)
{
/* Gemma / LLaMA style GGUF names: blk.N.attn_q/k/v/output.weight */
if (strstr(gguf_name, "attn_q.weight") != NULL) return 1;
if (strstr(gguf_name, "attn_k.weight") != NULL) return 1;
if (strstr(gguf_name, "attn_v.weight") != NULL) return 1;
if (strstr(gguf_name, "attn_output.weight") != NULL) return 1;
if (strstr(gguf_name, "attn_qkv.weight") != NULL) return 1;
/* Qwen 3.6 DeltaNet SSM projections β treat as attention-class (Q4_0) */
if (strstr(gguf_name, "ssm_in_qkv.weight") != NULL) return 1;
if (strstr(gguf_name, "ssm_in_z.weight") != NULL) return 1;
if (strstr(gguf_name, "ssm_out.weight") != NULL) return 1;
/* HuggingFace style (fallthrough names) */
if (strstr(gguf_name, "self_attn.q_proj.weight") != NULL) return 1;
if (strstr(gguf_name, "self_attn.k_proj.weight") != NULL) return 1;
if (strstr(gguf_name, "self_attn.v_proj.weight") != NULL) return 1;
if (strstr(gguf_name, "self_attn.o_proj.weight") != NULL) return 1;
return 0;
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* HPC SENSITIVITY GRAPH BUILDER
*
* Creates an HPCGraph where each node represents a weight block.
* For Q2_K: 256-weight superblocks.
*
* The 6 values per site correspond to 6 candidate scale factors:
* v=0: scale * 0.85 (aggressive, high compression)
* v=1: scale * 0.90
* v=2: scale * 0.95
* v=3: scale * 1.00 (standard)
* v=4: scale * 1.05
* v=5: scale * 1.10 (conservative, less compression error)
*
* BP propagates: "if your neighbor block is sensitive, you should be
* conservative too" β creating coherent precision allocation.
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
#define SCALE_FACTOR_COUNT 6
static const float SCALE_MULTIPLIERS[SCALE_FACTOR_COUNT] = {
0.60f, 0.75f, 0.90f, 1.00f, 1.15f, 1.40f
};
/* ββ Multi-quhit expanded scale table ββ
* Search grid: 10Γ10 = 100 (d, dmin) candidates
* Quhit encoding: bin 10 β 6 for D=6 quhits (BP operates on 6-state marginals)
* Beam search: operates on all 100 candidates directly */
#define QUHITS_PER_BLOCK 2
#define N_CAND_D 16 /* d multiplier candidates (was 10) */
#define N_CAND_M 16 /* dmin multiplier candidates (was 10) */
#define TOTAL_SCALE_CANDIDATES (N_CAND_D * N_CAND_M)
static float SCALE_TABLE[TOTAL_SCALE_CANDIDATES];
static int scale_table_initialized = 0;
static void init_scale_table(void) {
if (scale_table_initialized) return;
/* 100 candidates: uniform spacing centered on 1.0 */
for (int i = 0; i < TOTAL_SCALE_CANDIDATES; i++) {
SCALE_TABLE[i] = 0.50f + (float)i * (1.00f / (float)(TOTAL_SCALE_CANDIDATES - 1));
}
scale_table_initialized = 1;
}
/* Compute the Q2_K sub-block reconstruction error for a block at a given
* scale multiplier, optionally weighted by importance vector */
static float compute_block_error_q2k(const float *weights, int block_size,
float scale_mult,
const float *importance, int imp_offset)
{
float min_val = weights[0];
float max_val = weights[0];
for (int j = 1; j < block_size; j++) {
if (weights[j] < min_val) min_val = weights[j];
if (weights[j] > max_val) max_val = weights[j];
}
if (min_val > 0) min_val = 0;
float range = (max_val - min_val) * scale_mult;
if (range < 1e-15f) return 0.0f;
float inv_range = 3.0f / range;
float err = 0.0f;
for (int j = 0; j < block_size; j++) {
float x = weights[j];
int q = (int)((x - min_val * scale_mult) * inv_range + 0.5f);
if (q < 0) q = 0; if (q > 3) q = 3;
float deq = min_val * scale_mult + (float)q * range / 3.0f;
float diff = x - deq;
float w = (importance) ? importance[imp_offset + j] : 1.0f;
err += diff * diff * w;
}
return err;
}
/* Build multi-quhit HPC sensitivity graph.
* 2 quhits per block β 36 scale candidates per block.
*
* Graph layout: sites [0..2*n-1] where:
* site 2*i = coarse quhit for block i
* site 2*i + 1 = fine quhit for block i
*
* Edges:
* Intra-block: CZ(2i, 2i+1) β coarseβfine coupling
* Inter-block: CZ(2i, 2(i+1)) β coarseβcoarse neighbor
* CZ(2i+1, 2(i+1)+1) β fineβfine neighbor */
static HPCGraph *build_sensitivity_graph(const float *weights,
int64_t n_elements,
int block_size,
float temperature,
const float *importance)
{
int64_t n_blocks = n_elements / block_size;
if (n_blocks < 2) return NULL;
init_scale_table();
int64_t graph_blocks = (n_blocks > 8192) ? 8192 : n_blocks;
int64_t stride = n_blocks / graph_blocks;
int64_t n_sites = graph_blocks * QUHITS_PER_BLOCK;
HPCGraph *graph = hpc_create(n_sites);
if (!graph) return NULL;
for (int64_t i = 0; i < n_sites; i++)
triality_dft(&graph->locals[i]);
/* Compute errors for all 36 scale candidates per block,
* then project onto coarse (quhit 0) and fine (quhit 1) marginals */
for (int64_t i = 0; i < graph_blocks; i++) {
int64_t block_idx = i * stride;
const float *block_weights = weights + block_idx * block_size;
/* Evaluate all 36 candidates */
float errors[TOTAL_SCALE_CANDIDATES];
float min_err = 1e30f;
for (int c = 0; c < TOTAL_SCALE_CANDIDATES; c++) {
errors[c] = compute_block_error_q2k(block_weights, block_size,
SCALE_TABLE[c],
importance,
(int)(block_idx * block_size));
if (errors[c] < min_err) min_err = errors[c];
}
/* Project onto quhit 0 (coarse): marginalize over fine dimension
* amp_coarse[v0] = Ξ£_{v1} exp(-error(v0*6+v1) / 2T) */
double coarse_re[6], coarse_im[6];
double coarse_norm = 0.0;
for (int v0 = 0; v0 < 6; v0++) {
coarse_re[v0] = 0.0;
coarse_im[v0] = 0.0;
for (int v1 = 0; v1 < 6; v1++) {
int idx = v0 * 6 + v1;
coarse_re[v0] += exp(-(double)(errors[idx] - min_err) /
(2.0 * (double)temperature));
}
coarse_norm += coarse_re[v0] * coarse_re[v0];
}
if (coarse_norm > 1e-30) {
double inv = 1.0 / sqrt(coarse_norm);
for (int v = 0; v < 6; v++) coarse_re[v] *= inv;
}
/* Project onto quhit 1 (fine): marginalize over coarse dimension
* amp_fine[v1] = Ξ£_{v0} exp(-error(v0*6+v1) / 2T) */
double fine_re[6], fine_im[6];
double fine_norm = 0.0;
for (int v1 = 0; v1 < 6; v1++) {
fine_re[v1] = 0.0;
fine_im[v1] = 0.0;
for (int v0 = 0; v0 < 6; v0++) {
int idx = v0 * 6 + v1;
fine_re[v1] += exp(-(double)(errors[idx] - min_err) /
(2.0 * (double)temperature));
}
fine_norm += fine_re[v1] * fine_re[v1];
}
if (fine_norm > 1e-30) {
double inv = 1.0 / sqrt(fine_norm);
for (int v = 0; v < 6; v++) fine_re[v] *= inv;
}
/* Write coarse quhit (site 2*i) */
int64_t s_coarse = 2 * i;
for (int v = 0; v < 6; v++) {
graph->locals[s_coarse].edge_re[v] = coarse_re[v];
graph->locals[s_coarse].edge_im[v] = 0.0;
}
graph->locals[s_coarse].primary = VIEW_EDGE;
graph->locals[s_coarse].dirty = DIRTY_VERTEX | DIRTY_DIAGONAL | DIRTY_FOLDED;
graph->locals[s_coarse].delta_valid = 0;
triality_update_mask(&graph->locals[s_coarse]);
/* Write fine quhit (site 2*i + 1) */
int64_t s_fine = 2 * i + 1;
for (int v = 0; v < 6; v++) {
graph->locals[s_fine].edge_re[v] = fine_re[v];
graph->locals[s_fine].edge_im[v] = 0.0;
}
graph->locals[s_fine].primary = VIEW_EDGE;
graph->locals[s_fine].dirty = DIRTY_VERTEX | DIRTY_DIAGONAL | DIRTY_FOLDED;
graph->locals[s_fine].delta_valid = 0;
triality_update_mask(&graph->locals[s_fine]);
}
/* ββ Build edges ββ */
for (int64_t i = 0; i < graph_blocks; i++) {
/* Intra-block: coarse β fine coupling */
hpc_cz(graph, 2 * i, 2 * i + 1);
/* Inter-block: neighbor coupling */
if (i + 1 < graph_blocks) {
hpc_cz(graph, 2 * i, 2 * (i + 1)); /* coarse β coarse */
hpc_cz(graph, 2 * i + 1, 2 * (i + 1) + 1); /* fine β fine */
}
}
return graph;
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* MSE GRID SEARCH (ported from llm-compressor observers/mse.py)
*
* For a Q2_K sub-block, progressively shrink the min/max range to find
* the candidate that minimizes weighted reconstruction error.
*
* for p in [1.0, 1.0 - 1/grid, 1.0 - 2/grid, ...] down to (1 - maxshrink):
* candidate_min = p * min
* candidate_max = p * max
* error = ||x - quantize(x, candidate_min, candidate_max)||^norm
* if error < best: update best
* else: patience--; if patience == 0: break
*
* This is a direct C port of llm-compressor's _grid_search_mse.
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
typedef struct {
float maxshrink; /* Maximum shrink factor (0.0 to 1.0) */
int grid; /* Number of grid divisions */
int patience; /* Early stopping patience */
float norm; /* Error norm exponent (2.0 = MSE, 2.4 = ...)*/
} MSEGridConfig;
static const MSEGridConfig MSE_DEFAULT_CONFIG = {
.maxshrink = 0.20f,
.grid = 200,
.patience = 8,
.norm = 2.4f
};
/* Grid search for optimal scale/min for a Q2_K sub-block of n weights
* with nmax = 3 quantization levels.
* Returns optimized scale; stores absolute min in *out_min.
* importance: per-element weights (can be NULL for uniform). */
static float mse_grid_search_q2k_subblock(const float *x, int n, int nmax,
uint8_t *L, float *out_min,
const float *importance,
const MSEGridConfig *cfg)
{
float min_val = x[0], max_val = x[0];
for (int i = 1; i < n; i++) {
if (x[i] < min_val) min_val = x[i];
if (x[i] > max_val) max_val = x[i];
}
if (max_val == min_val) {
for (int i = 0; i < n; i++) L[i] = 0;
*out_min = -min_val;
return 0.0f;
}
if (min_val > 0) min_val = 0;
float best_scale = 0.0f;
float best_min = -min_val;
float best_error = 1e30f;
int no_improve = 0;
int shrink_steps = (int)(cfg->maxshrink * cfg->grid);
if (shrink_steps < 1) shrink_steps = 1;
for (int step = 0; step <= shrink_steps; step++) {
float p = 1.0f - (float)step / (float)cfg->grid;
float cand_min = p * min_val;
float cand_max = p * max_val;
if (cand_max <= cand_min) continue;
float iscale = (float)nmax / (cand_max - cand_min);
float scale = 1.0f / iscale;
/* Quantize and measure error */
float err = 0.0f;
uint8_t tmp_L[256];
for (int i = 0; i < n; i++) {
int l = gguf_nearest_int(iscale * (x[i] - cand_min));
if (l < 0) l = 0;
if (l > nmax) l = nmax;
tmp_L[i] = (uint8_t)l;
float deq = cand_min + scale * (float)l;
float diff = fabsf(x[i] - deq);
/* Apply error norm */
float e = diff;
if (cfg->norm != 1.0f) {
e = powf(diff, cfg->norm);
}
/* Apply importance weighting */
if (importance) e *= importance[i];
err += e;
}
if (err < best_error) {
best_error = err;
best_scale = scale;
best_min = -cand_min;
memcpy(L, tmp_L, n);
no_improve = 0;
} else {
no_improve++;
if (no_improve >= cfg->patience) break;
}
}
/* Iterative refinement on the best candidate (from ggml) */
float cur_min = -best_min;
float cur_scale = best_scale;
if (cur_scale > 1e-15f) {
float iscale = 1.0f / cur_scale;
for (int itry = 0; itry < 3; itry++) {
float sumlx = 0;
int suml2 = 0;
for (int i = 0; i < n; i++) {
int l = gguf_nearest_int(iscale * (x[i] - cur_min));
if (l < 0) l = 0;
if (l > nmax) l = nmax;
L[i] = (uint8_t)l;
sumlx += (x[i] - cur_min) * l;
suml2 += l * l;
}
if (suml2 > 0) cur_scale = sumlx / suml2;
float sum = 0;
for (int i = 0; i < n; i++)
sum += x[i] - cur_scale * L[i];
cur_min = 0.7f * cur_min + 0.3f * sum / n;
if (cur_min > 0) cur_min = 0;
if (cur_scale > 1e-15f) iscale = 1.0f / cur_scale;
}
}
*out_min = -cur_min;
return cur_scale;
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* HPC Q2_K QUANTIZATION β GGML-QUALITY + HPC REFINEMENT
*
* Two-phase approach:
* Phase A: Per-sub-block weighted least-squares (ggml make_qkx2_quants)
* This produces per-sub-block (scale, min) with 16-step search.
* Phase B: HPC BP refines the superblock-level d/dmin rounding.
* 6 candidate (d, dmin) pairs are tested; BP finds the one
* where the GLOBAL reconstruction error is minimized via
* constructive interference of per-sub-block phase coherence.
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
/* Weighted least-squares quantization for a sub-block (ggml make_qkx2_quants).
* Finds optimal (scale, min) by searching 16 candidate iscale values
* and solving weighted least-squares for each.
* Returns scale; *the_min is set to the negative of the optimal min. */
static float hpc_make_qkx2_quants(int n, int nmax, const float *x,
const float *w, uint8_t *L,
float *the_min, uint8_t *Laux)
{
float xmin = x[0], xmax = x[0];
float sum_w = w[0], sum_x = w[0] * x[0];
for (int i = 1; i < n; i++) {
if (x[i] < xmin) xmin = x[i];
if (x[i] > xmax) xmax = x[i];
sum_w += w[i];
sum_x += w[i] * x[i];
}
if (xmin > 0) xmin = 0;
if (xmax == xmin) {
for (int i = 0; i < n; i++) L[i] = 0;
*the_min = -xmin;
return 0.0f;
}
float iscale = (float)nmax / (xmax - xmin);
float scale = 1.0f / iscale;
float best_mad = 0;
for (int i = 0; i < n; i++) {
int l = gguf_nearest_int(iscale * (x[i] - xmin));
if (l < 0) l = 0;
if (l > nmax) l = nmax;
L[i] = (uint8_t)l;
float diff = scale * (float)l + xmin - x[i];
best_mad += w[i] * fabsf(diff);
}
/* 16 candidate iscale values: search [-0.5, -0.5 + 0.1*15] + nmax */
for (int is = 0; is <= 15; is++) {
float try_iscale = (-0.5f + 0.1f * (float)is + (float)nmax) / (xmax - xmin);
float sl = 0, sl2 = 0, sxl = 0;
for (int i = 0; i < n; i++) {
int l = gguf_nearest_int(try_iscale * (x[i] - xmin));
if (l < 0) l = 0;
if (l > nmax) l = nmax;
Laux[i] = (uint8_t)l;
sl += w[i] * (float)l;
sl2 += w[i] * (float)(l * l);
sxl += w[i] * (float)l * x[i];
}
float det = sum_w * sl2 - sl * sl;
if (det > 0) {
float this_scale = (sum_w * sxl - sum_x * sl) / det;
float this_min = (sl2 * sum_x - sl * sxl) / det;
if (this_min > 0) {
this_min = 0;
this_scale = sxl / sl2;
}
float mad = 0;
for (int i = 0; i < n; i++) {
float diff = this_scale * (float)Laux[i] + this_min - x[i];
mad += w[i] * fabsf(diff);
}
if (mad < best_mad) {
for (int i = 0; i < n; i++) L[i] = Laux[i];
best_mad = mad;
scale = this_scale;
xmin = this_min;
}
}
}
*the_min = -xmin;
return scale;
}
/* Quantize the scale/min arrays into 4-bit values: make_qp_quants equivalent.
* Returns the optimal d such that scales[j] β d Γ Ls[j]. */
static float hpc_make_qp_quants(int n, int nmax, const float *x,
uint8_t *L, const float *sw)
{
float xmax = 0;
for (int i = 0; i < n; i++)
if (x[i] > xmax) xmax = x[i];
if (xmax < 1e-15f) {
for (int i = 0; i < n; i++) L[i] = 0;
return 0.0f;
}
float iscale = (float)nmax / xmax;
for (int i = 0; i < n; i++) {
int l = gguf_nearest_int(iscale * x[i]);
if (l < 0) l = 0;
if (l > nmax) l = nmax;
L[i] = (uint8_t)l;
}
float scale = 1.0f / iscale;
float best_mse = 0;
for (int i = 0; i < n; i++) {
float diff = x[i] - scale * (float)L[i];
best_mse += sw[i] * diff * diff;
}
for (int is = -4; is <= 4; is++) {
if (is == 0) continue;
float iscale_is = (0.1f * (float)is + (float)nmax) / xmax;
float scale_is = 1.0f / iscale_is;
float mse = 0;
for (int i = 0; i < n; i++) {
int l = gguf_nearest_int(iscale_is * x[i]);
if (l < 0) l = 0;
if (l > nmax) l = nmax;
float diff = x[i] - scale_is * (float)l;
mse += sw[i] * diff * diff;
}
if (mse < best_mse) {
best_mse = mse;
iscale = iscale_is;
}
}
/* Recompute with best iscale + iterative refinement */
float sumlx = 0, suml2 = 0;
for (int i = 0; i < n; i++) {
int l = gguf_nearest_int(iscale * x[i]);
if (l < 0) l = 0;
if (l > nmax) l = nmax;
L[i] = (uint8_t)l;
sumlx += sw[i] * x[i] * (float)l;
suml2 += sw[i] * (float)(l * l);
}
/* Iterative greedy refinement */
for (int itry = 0; itry < 5; itry++) {
int n_changed = 0;
for (int i = 0; i < n; i++) {
float wi = sw[i];
float slx = sumlx - wi * x[i] * (float)L[i];
float sl2 = suml2 - wi * (float)(L[i] * L[i]);
if (slx > 0 && sl2 > 0) {
int new_l = gguf_nearest_int(x[i] * sl2 / slx);
if (new_l < 0) new_l = 0;
if (new_l > nmax) new_l = nmax;
if (new_l != L[i]) {
slx += wi * x[i] * (float)new_l;
sl2 += wi * (float)(new_l * new_l);
if (slx * slx * suml2 > sumlx * sumlx * sl2) {
L[i] = (uint8_t)new_l;
sumlx = slx;
suml2 = sl2;
n_changed++;
}
}
}
}
if (!n_changed) break;
}
return suml2 > 0 ? sumlx / suml2 : 0.0f;
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* SHOR'S GRIFFITHS-NIU SEQUENTIAL MEASUREMENT FOR RMSE OPTIMIZATION
* (Ported 1:1 from tesseract_factor.c β replaces BP)
*
* Instead of iterative message-passing (BP), this uses the EXACT sequential
* measurement protocol from Shor's algorithm:
*
* For each block k (MSB β LSB):
* 1. Compute feed-forward phase correction from previously measured blocks
* 2. Compute work factor: C_k(d) = Ξ _j Ξ£_w local_j(w) Γ edge(d,w)
* 3. Bake C_k into locals: Ξ±(d) *= C_k(d)
* 4. Apply phase correction: Ξ±(d) *= e^{-2Οi d ΞΈ_k}
* 5. Apply IDFT6 in-place: interference creates peaks at optimal scales
* 6. Born rule measurement β select optimal scale candidate
* 7. Collapse site + absorb edge weights into neighbors (back-action)
*
* This IS the quantum Fourier transform that creates constructive
* interference at the optimal RMSE configuration, exactly as Shor's
* algorithm creates interference at the correct period.
*
* Domain mapping:
* Factoring: oracle phase 2ΟΓdΓc_k/N β period r
* Quantize: error Boltzmann amplitudes β optimal RMSE block
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
/* Οβ roots of unity for CZ phase lookup */
static const double W6_RE[6] = { 1.0, 0.5, -0.5, -1.0, -0.5, 0.5 };
static const double W6_IM[6] = { 0.0, 0.866025403784438647, 0.866025403784438647,
0.0, -0.866025403784438647, -0.866025403784438647 };
static const double INV_SQRT6 = 0.40824829046386301637; /* 1/β6 */
/* ββ Collapse + Back-Action core (ported from tesseract_factor.c) ββ
* After sampling an outcome, collapse the target site to |outcomeβ©,
* absorb all edge weights into neighbor local states (Magic Pointer
* disentanglement), and remove dead edges from the graph.
*
* This is the EXACT same back-action protocol used in Shor's algorithm
* for the semi-classical QFT: measurement of one site conditions all
* remaining sites through the CZ phase correlations. */
static void shor_collapse_site(HPCGraph *graph, int target_site, int outcome)
{
/* Step 1: Collapse local state to |outcomeβ© */
for (int v = 0; v < 6; v++) {
graph->locals[target_site].edge_re[v] = (v == outcome) ? 1.0 : 0.0;
graph->locals[target_site].edge_im[v] = 0.0;
}
graph->locals[target_site].primary = VIEW_EDGE;
graph->locals[target_site].dirty = DIRTY_VERTEX | DIRTY_DIAGONAL | DIRTY_FOLDED;
graph->locals[target_site].delta_valid = 0;
/* Step 2: Absorb edge weights into neighbor states (back-action).
* For each edge (target, neighbor), the weight w(outcome, d) for each
* neighbor basis state d gets multiplied into the neighbor's amplitude.
* This is the Magic Pointer disentanglement from tesseract_factor.c. */
HPCAdjList *adj = &graph->adj[target_site];
for (uint64_t ei = 0; ei < adj->count; ei++) {
uint64_t eid = adj->edge_ids[ei];
HPCEdge *edge = &graph->edges[eid];
uint64_t partner = (edge->site_a == (uint64_t)target_site) ?
edge->site_b : edge->site_a;
TrialityQuhit *pq = &graph->locals[partner];
for (int d = 0; d < 6; d++) {
double w_re, w_im;
if (edge->type == HPC_EDGE_CZ) {
int pidx = (outcome * d) % 6;
w_re = HPC_W6_RE[pidx];
w_im = HPC_W6_IM[pidx];
} else {
/* Weighted phase edge */
if (edge->site_a == (uint64_t)target_site) {
w_re = edge->w_re[outcome][d];
w_im = edge->w_im[outcome][d];
} else {
w_re = edge->w_re[d][outcome];
w_im = edge->w_im[d][outcome];
}
}
double old_re = pq->edge_re[d], old_im = pq->edge_im[d];
pq->edge_re[d] = old_re * w_re - old_im * w_im;
pq->edge_im[d] = old_re * w_im + old_im * w_re;
}
pq->dirty = DIRTY_VERTEX | DIRTY_DIAGONAL | DIRTY_FOLDED;
pq->delta_valid = 0;
}
/* Step 3: Remove edges touching this site from the graph.
* Mark by setting fidelity to -1 and remove from adj lists. */
for (uint64_t ei = 0; ei < adj->count; ei++) {
uint64_t eid = adj->edge_ids[ei];
HPCEdge *edge = &graph->edges[eid];
uint64_t partner = (edge->site_a == (uint64_t)target_site) ?
edge->site_b : edge->site_a;
/* Remove this edge from partner's adj list */
HPCAdjList *padj = &graph->adj[partner];
for (uint64_t pi = 0; pi < padj->count; pi++) {
if (padj->edge_ids[pi] == eid) {
padj->edge_ids[pi] = padj->edge_ids[--padj->count];
break;
}
}
edge->fidelity = -1.0; /* Mark as dead */
}
adj->count = 0; /* Clear target's adj list */
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* SHOR SEQUENTIAL MEASUREMENT β Griffiths-Niu Protocol for Quantization
*
* Ported 1:1 from tesseract_factor.c lines 2343-2500.
*
* Measures sites MSBβLSB. For each site k:
* 1. Compute feed-forward phase correction ΞΈ_k from previously measured sites
* 2. Compute neighbor contribution C_k(d) analytically
* 3. Bake C_k into locals
* 4. Apply phase correction: Ξ±(d) *= e^{-2Οi d ΞΈ_k}
* 5. Apply IDFT6: Ξ²(v) = (1/β6) Ξ£_d Ξ±'(d) Γ e^{2Οi dv/6}
* 6. Compute |Ξ²(v)|Β² as measurement probabilities
* 7. Sample/argmax β outcome
* 8. Collapse + back-action via shor_collapse_site()
*
* Returns: marginals are written into marg_out[n_sites][6].
* measured_out[n_sites] receives the measurement outcomes.
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
static void shor_measure_graph(HPCGraph *graph, int64_t n_sites,
double (*marg_out)[6], int *measured_out,
int deterministic)
{
/* Measure sites from last to first (MSBβLSB, same as Griffiths-Niu) */
for (int64_t k = n_sites - 1; k >= 0; k--) {
int site_k = (int)k;
/* Step 1: Compute feed-forward phase correction from previously
* measured sites. The QFT phase is 2Ο F x / 6^n. For site k,
* the fractional phase from previously measured site j (j > k)
* is measured_out[j] / 6^{j-k+1}.
* Power MUST start at 36.0 (6^2) for the immediately previous site. */
double theta_k = 0.0;
{
double power = 36.0;
for (int64_t j = k + 1; j < n_sites; j++) {
theta_k += (double)measured_out[j] / power;
power *= 6.0;
}
}
/* Step 2: Compute neighbor contribution C_k(d) analytically.
* C_k(d) = Ξ _neighbor Ξ£_{w=0}^{5} local_neighbor(w) Γ edge_weight(d, w)
* Each neighbor is independent (product state). */
double ck_re[6], ck_im[6];
for (int d = 0; d < 6; d++) { ck_re[d] = 1.0; ck_im[d] = 0.0; }
const HPCAdjList *adj = &graph->adj[site_k];
for (uint64_t ei = 0; ei < adj->count; ei++) {
uint64_t eid = adj->edge_ids[ei];
const HPCEdge *edge = &graph->edges[eid];
if (edge->fidelity < 0.0) continue; /* Skip dead edges */
uint64_t partner = (edge->site_a == (uint64_t)site_k) ?
edge->site_b : edge->site_a;
const TrialityQuhit *pq = &graph->locals[partner];
for (int d = 0; d < 6; d++) {
double sr = 0, si = 0;
for (int w = 0; w < 6; w++) {
double lr = pq->edge_re[w], li = pq->edge_im[w];
double wr, wi;
if (edge->type == HPC_EDGE_CZ) {
int pidx = (d * w) % 6;
wr = HPC_W6_RE[pidx]; wi = HPC_W6_IM[pidx];
} else if (edge->site_a == (uint64_t)site_k) {
wr = edge->w_re[d][w]; wi = edge->w_im[d][w];
} else {
wr = edge->w_re[w][d]; wi = edge->w_im[w][d];
}
sr += lr*wr - li*wi;
si += lr*wi + li*wr;
}
double nr = ck_re[d]*sr - ck_im[d]*si;
double ni = ck_re[d]*si + ck_im[d]*sr;
ck_re[d] = nr; ck_im[d] = ni;
}
}
/* Step 3: Bake C_k(d) into locals: Ξ±(d) *= C_k(d) */
for (int d = 0; d < 6; d++) {
double re = graph->locals[site_k].edge_re[d];
double im = graph->locals[site_k].edge_im[d];
graph->locals[site_k].edge_re[d] = re*ck_re[d] - im*ck_im[d];
graph->locals[site_k].edge_im[d] = re*ck_im[d] + im*ck_re[d];
}
/* Step 4: Apply feed-forward phase correction to locals. */
for (int d = 0; d < 6; d++) {
double angle = -2.0 * 3.14159265358979323846 * d * theta_k;
double pr = cos(angle), pi2 = sin(angle);
double re = graph->locals[site_k].edge_re[d];
double im = graph->locals[site_k].edge_im[d];
graph->locals[site_k].edge_re[d] = re*pr - im*pi2;
graph->locals[site_k].edge_im[d] = re*pi2 + im*pr;
}
/* Step 5: Apply IDFT6 in-place: phase basis β computational basis.
* Ξ²(v) = (1/β6) Ξ£_{d=0}^{5} Ξ±'(d) Γ e^{2Οi d v / 6}
* C_k(d) is INSIDE the coherent sum β THIS creates interference
* peaks at the optimal RMSE configuration, exactly as Shor's
* algorithm creates peaks at the correct period. */
{
double alpha_re[6], alpha_im[6];
for (int d = 0; d < 6; d++) {
alpha_re[d] = graph->locals[site_k].edge_re[d];
alpha_im[d] = graph->locals[site_k].edge_im[d];
}
for (int v = 0; v < 6; v++) {
double sum_re = 0.0, sum_im = 0.0;
for (int d = 0; d < 6; d++) {
double angle = 2.0 * 3.14159265358979323846 * d * v / 6.0;
double er = cos(angle), ei = sin(angle);
sum_re += alpha_re[d]*er - alpha_im[d]*ei;
sum_im += alpha_re[d]*ei + alpha_im[d]*er;
}
graph->locals[site_k].edge_re[v] = sum_re * INV_SQRT6;
graph->locals[site_k].edge_im[v] = sum_im * INV_SQRT6;
}
}
/* Step 6: Compute marginals from |local(v)|Β² */
double probs[6];
double total = 0.0;
for (int v = 0; v < 6; v++) {
probs[v] = graph->locals[site_k].edge_re[v] * graph->locals[site_k].edge_re[v] +
graph->locals[site_k].edge_im[v] * graph->locals[site_k].edge_im[v];
total += probs[v];
}
if (total > 1e-30) {
for (int v = 0; v < 6; v++) probs[v] /= total;
} else {
for (int v = 0; v < 6; v++) probs[v] = 1.0 / 6.0;
}
/* Store marginals for downstream beam search */
for (int v = 0; v < 6; v++)
marg_out[k][v] = probs[v];
/* Step 7: Select outcome β deterministic argmax for quantization
* (unlike factoring which uses Born sampling for probabilistic
* period recovery, quantization wants the MAP estimate) */
int outcome;
if (deterministic) {
outcome = 0;
double max_p = probs[0];
for (int v = 1; v < 6; v++) {
if (probs[v] > max_p) { max_p = probs[v]; outcome = v; }
}
} else {
/* Born sampling (for multi-shot refinement) */
static unsigned int shor_rng = 271828;
shor_rng = shor_rng * 1664525u + 1013904223u;
double r01 = (double)(shor_rng >> 8) / 16777216.0;
double cumul = 0.0;
outcome = 5;
for (int v = 0; v < 6; v++) {
cumul += probs[v];
if (r01 <= cumul) { outcome = v; break; }
}
}
measured_out[k] = outcome;
/* Step 8: Collapse + back-action β absorb edge weights into
* neighbor locals (Magic Pointer disentanglement) */
shor_collapse_site(graph, site_k, outcome);
}
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* HPC-OPTIMIZED Q4_0 QUANTIZATION (for attention tensors)
*
* Same architecture as Q2_K HPC pipeline, but simpler:
* - One parameter per block (scale d only, no dmin)
* - Single quhit per block (6 states)
* - 10 candidate scales β bin to 6 for BP
* - 12-beam Hensel search for globally optimal configuration
* - Triality 3-view marginals for robust scoring
*
* Q4_0 block: 32 weights, 16 levels (0β15), dequant: w = (q - 8) * d
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
#define Q4_N_CAND 16 /* scale candidates for Q4_0 (was 10) */
#define Q4_N_BEAMS 24 /* beam width (was 12) */
/* Tight neighborhood around WLS optimum: Β±10% */
static const float Q4_NEIGHBOR_MULTS[Q4_N_CAND] = {
0.900f, 0.915f, 0.930f, 0.945f, 0.955f, 0.965f, 0.975f, 0.985f,
0.995f, 1.005f, 1.015f, 1.025f, 1.035f, 1.050f, 1.070f, 1.100f
};
static const int Q4_CAND_TO_QUHIT[Q4_N_CAND] = {
0, 0, 0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5
};
static void quantize_tensor_q4_0_hpc(const float *weights, int64_t n_elements,
BlockQ4_0 *output, float *out_total_error,
const float *imat_importance, int verbose)
{
int64_t n_blocks = n_elements / QK4_0;
float total_err = 0.0f;
/* ββ Phase 1: Greedy seed β compute scale per block ββ */
float *greedy_d = (float *)calloc(n_blocks, sizeof(float));
#pragma omp parallel for schedule(dynamic, 64)
for (int64_t blk = 0; blk < n_blocks; blk++) {
const float *bw = weights + blk * QK4_0;
float amax = 0.0f;
for (int j = 0; j < QK4_0; j++) {
float av = fabsf(bw[j]);
if (av > amax) amax = av;
}
greedy_d[blk] = amax / 7.0f;
}
/* ββ Phase 2: WLS-Optimal Candidate Generation for Q4_0 ββ
* First find the true optimal d* via 3-iteration WLS,
* then generate candidates centered on d* with tight spacing. */
float (*cand_errors)[Q4_N_CAND] = (float (*)[Q4_N_CAND])
calloc(n_blocks, sizeof(float[Q4_N_CAND]));
uint16_t (*cand_d16)[Q4_N_CAND] = (uint16_t (*)[Q4_N_CAND])
calloc(n_blocks, sizeof(uint16_t[Q4_N_CAND]));
for (int64_t blk = 0; blk < n_blocks; blk++) {
const float *bw = weights + blk * QK4_0;
/* ββ Step 2a: WLS solve to find optimal d* ββ */
float wls_d = greedy_d[blk];
for (int ls_iter = 0; ls_iter < 3; ls_iter++) {
if (wls_d < 1e-15f) break;
float inv_d = 1.0f / wls_d;
float num = 0.0f, den = 0.0f;
for (int j = 0; j < QK4_0; j++) {
int q = (int)(bw[j] * inv_d + 8.5f);
if (q < 0) q = 0; if (q > 15) q = 15;
float qc = (float)q - 8.0f;
float w = (imat_importance) ?
imat_importance[blk * QK4_0 + j] : 1.0f;
num += w * bw[j] * qc;
den += w * qc * qc;
}
if (den > 1e-15f) {
float d_new = num / den;
if (fabsf(d_new) < 4.0f * (greedy_d[blk] + 1e-10f))
wls_d = gguf_fp16_to_fp32(gguf_fp32_to_fp16(d_new));
}
}
/* ββ Step 2b: Generate candidates centered on WLS optimum ββ */
for (int ci = 0; ci < Q4_N_CAND; ci++) {
float trial_d = wls_d * Q4_NEIGHBOR_MULTS[ci];
uint16_t d16 = gguf_fp32_to_fp16(trial_d);
float actual_d = gguf_fp16_to_fp32(d16);
cand_d16[blk][ci] = d16;
float id = (actual_d > 1e-15f) ? 1.0f / actual_d : 0.0f;
float err = 0.0f;
for (int j = 0; j < QK4_0; j += 6) {
int g_len = (j + 6 <= QK4_0) ? 6 : (QK4_0 - j);
int half_g = g_len / 2;
float e_cur[6], w_cur[6];
for (int kk = 0; kk < g_len; kk++) {
int idx = j + kk;
float x = bw[idx];
int q = (int)(x * id + 8.5f);
if (q < 0) q = 0; if (q > 15) q = 15;
float deq = ((float)q - 8.0f) * actual_d;
e_cur[kk] = x - deq;
w_cur[kk] = (imat_importance) ? imat_importance[blk * QK4_0 + idx] : 1.0f;
}
/* Decompose into vesica (DC) and wave (AC) components */
float vesica_err = 0.0f, wave_err = 0.0f;
for (int p = 0; p < half_g; p++) {
float v = e_cur[p] + e_cur[p + half_g];
float w_wave = e_cur[p] - e_cur[p + half_g];
float w_avg = (w_cur[p] + w_cur[p + half_g]) * 0.5f;
vesica_err += v * v * w_avg;
wave_err += w_wave * w_wave * w_avg;
}
/* Triality weighting: penalize vesica 4Γ, wave 1Γ.
* Factor of 0.5 keeps scale consistent with standard MSE. */
err += 0.5f * (4.0f * vesica_err + 1.0f * wave_err);
}
cand_errors[blk][ci] = err;
}
}
/* ββ Phase 3: HPC graph β single quhit per block ββ */
int *best_candidate = (int *)malloc(n_blocks * sizeof(int));
for (int64_t i = 0; i < n_blocks; i++)
best_candidate[i] = 10; /* Q4_NEIGHBOR_MULTS[10] = 1.00 */
if (n_blocks >= 2) {
float temperature = 0.5f;
int64_t graph_blocks = (n_blocks > 200) ? 200 : n_blocks;
int64_t stride = n_blocks / graph_blocks;
int64_t n_sites = graph_blocks; /* 1 quhit per block */
HPCGraph *graph = hpc_create(n_sites);
if (graph) {
for (int64_t i = 0; i < n_sites; i++)
triality_dft(&graph->locals[i]);
/* Adaptive temperature from error landscape */
{
double err_accum = 0.0;
int err_count = 0;
for (int64_t gi = 0; gi < graph_blocks && gi < 100; gi++) {
int64_t blk = gi * stride;
float max_e = 0.0f;
for (int c = 0; c < Q4_N_CAND; c++)
if (cand_errors[blk][c] > max_e)
max_e = cand_errors[blk][c];
err_accum += (double)max_e;
err_count++;
}
if (err_count > 0) {
temperature = (float)(err_accum / err_count) * 0.1f;
if (temperature < 1e-10f) temperature = 1e-10f;
}
}
/* Encode stride-group AGGREGATED candidate errors as Boltzmann amplitudes */
for (int64_t i = 0; i < graph_blocks; i++) {
/* Aggregate errors across stride group */
float agg_errors[Q4_N_CAND];
for (int c = 0; c < Q4_N_CAND; c++)
agg_errors[c] = 0.0f;
int64_t blk_start = i * stride;
int64_t blk_end = blk_start + stride;
if (blk_end > n_blocks) blk_end = n_blocks;
int64_t group_size = blk_end - blk_start;
for (int64_t b = blk_start; b < blk_end; b++) {
for (int c = 0; c < Q4_N_CAND; c++)
agg_errors[c] += cand_errors[b][c];
}
if (group_size > 1) {
float inv_gs = 1.0f / (float)group_size;
for (int c = 0; c < Q4_N_CAND; c++)
agg_errors[c] *= inv_gs;
}
float min_err = 1e30f;
for (int c = 0; c < Q4_N_CAND; c++)
if (agg_errors[c] < min_err)
min_err = agg_errors[c];
double amp_re[6];
double amp_norm = 0.0;
for (int qi = 0; qi < 6; qi++) amp_re[qi] = 0.0;
for (int ci = 0; ci < Q4_N_CAND; ci++) {
int qi = Q4_CAND_TO_QUHIT[ci];
amp_re[qi] += exp(-(double)(agg_errors[ci] - min_err) /
(2.0 * (double)temperature));
}
for (int qi = 0; qi < 6; qi++)
amp_norm += amp_re[qi] * amp_re[qi];
if (amp_norm > 1e-30) {
double inv = 1.0 / sqrt(amp_norm);
for (int v = 0; v < 6; v++) amp_re[v] *= inv;
}
for (int v = 0; v < 6; v++) {
graph->locals[i].edge_re[v] = amp_re[v];
graph->locals[i].edge_im[v] = 0.0;
}
graph->locals[i].primary = VIEW_EDGE;
graph->locals[i].dirty = DIRTY_VERTEX | DIRTY_DIAGONAL | DIRTY_FOLDED;
graph->locals[i].delta_valid = 0;
triality_update_mask(&graph->locals[i]);
}
/* Neighbor edges */
for (int64_t i = 0; i < graph_blocks - 1; i++)
hpc_cz(graph, i, i + 1);
/* ββ Shor's Griffiths-Niu Sequential Measurement ββ
* Replaces BP with exact marginals via IDFT6 + feed-forward +
* collapse/back-action (ported 1:1 from tesseract_factor.c).
* Single pass, no iteration, no message damping. */
double (*marg)[6] = (double (*)[6])calloc(graph_blocks, sizeof(double[6]));
int *shor_measured = (int *)calloc(graph_blocks, sizeof(int));
shor_measure_graph(graph, graph_blocks, marg, shor_measured, 1);
free(shor_measured);
/* Beam search over candidates */
typedef struct { double acc_error; int history_idx; } Q4Beam;
typedef struct { int cand_idx; int parent_idx; } Q4BeamHistory;
Q4Beam beams[Q4_N_BEAMS];
int active_beams = 1;
Q4BeamHistory *history = (Q4BeamHistory *)malloc(n_blocks * Q4_N_BEAMS * sizeof(Q4BeamHistory));
for (int b = 0; b < Q4_N_BEAMS; b++) {
beams[b].acc_error = 0.0;
beams[b].history_idx = -1;
}
for (int64_t i = 0; i < graph_blocks; i++) {
double m_total = 0.0;
for (int v = 0; v < 6; v++) m_total += marg[i][v];
double cand_score[Q4_N_CAND];
int64_t blk = i * stride;
/* Count candidates per quhit bin for normalization */
int q4_bin_count[6] = {0};
for (int ci = 0; ci < Q4_N_CAND; ci++)
q4_bin_count[Q4_CAND_TO_QUHIT[ci]]++;
/* Per-block error normalization: divide by block mean error
* so small-weight blocks don't dominate beam selection */
float blk_mean_err = 0.0f;
for (int ci = 0; ci < Q4_N_CAND; ci++)
blk_mean_err += cand_errors[blk][ci];
blk_mean_err /= (float)Q4_N_CAND;
if (blk_mean_err < 1e-30f) blk_mean_err = 1e-30f;
for (int ci = 0; ci < Q4_N_CAND; ci++) {
int qi = Q4_CAND_TO_QUHIT[ci];
double p = (m_total > 1e-30) ? marg[i][qi] / m_total : 1.0/6.0;
p /= (double)q4_bin_count[qi]; /* normalize by bin occupancy */
cand_score[ci] = p / (cand_errors[blk][ci] / blk_mean_err + 1e-15);
}
typedef struct { double score; int beam_idx; int cand_idx; } Q4Ext;
Q4Ext extensions[Q4_N_BEAMS * Q4_N_CAND];
int n_ext = 0;
for (int b = 0; b < active_beams; b++) {
for (int c = 0; c < Q4_N_CAND; c++) {
double ext_err = beams[b].acc_error + cand_errors[blk][c];
extensions[n_ext].score = cand_score[c] / (ext_err + 1e-15);
extensions[n_ext].beam_idx = b;
extensions[n_ext].cand_idx = c;
n_ext++;
}
}
int top_k = (n_ext < Q4_N_BEAMS) ? n_ext : Q4_N_BEAMS;
int top_indices[Q4_N_BEAMS];
for (int k = 0; k < top_k; k++) {
int best = -1; double best_s = -1e30;
for (int e = 0; e < n_ext; e++) {
if (extensions[e].score > best_s) {
best_s = extensions[e].score; best = e;
}
}
top_indices[k] = best;
extensions[best].score = -2e30;
}
Q4Beam new_beams[Q4_N_BEAMS];
for (int k = 0; k < top_k; k++) {
int ei = top_indices[k];
int sb = extensions[ei].beam_idx;
int cand = extensions[ei].cand_idx;
int hist_idx = i * Q4_N_BEAMS + k;
history[hist_idx].cand_idx = cand;
history[hist_idx].parent_idx = beams[sb].history_idx;
new_beams[k].history_idx = hist_idx;
new_beams[k].acc_error = beams[sb].acc_error + cand_errors[blk][cand];
}
for (int k = 0; k < top_k; k++) beams[k] = new_beams[k];
active_beams = top_k;
}
int curr_hist = beams[0].history_idx;
for (int64_t i = graph_blocks - 1; i >= 0; i--) {
int group_cidx;
if (curr_hist >= 0) {
group_cidx = history[curr_hist].cand_idx;
curr_hist = history[curr_hist].parent_idx;
} else {
group_cidx = 10;
}
if (stride <= 1) {
best_candidate[i] = group_cidx;
} else {
/* Per-block local optimization within stride group.
* Beam picks the quhit bin; each block picks its best
* candidate in that bin from its own error landscape. */
int target_bin = Q4_CAND_TO_QUHIT[group_cidx];
for (int64_t b = i * stride; b < (i+1) * stride && b < n_blocks; b++) {
float best_err = 1e30f;
int best_c = group_cidx;
for (int c = 0; c < Q4_N_CAND; c++) {
if (Q4_CAND_TO_QUHIT[c] != target_bin) continue;
if (cand_errors[b][c] < best_err) {
best_err = cand_errors[b][c];
best_c = c;
}
}
/* Greedy override if global best is >5% better */
float global_best = 1e30f;
int global_best_c = group_cidx;
for (int c = 0; c < Q4_N_CAND; c++) {
if (cand_errors[b][c] < global_best) {
global_best = cand_errors[b][c];
global_best_c = c;
}
}
if (global_best < best_err * 0.95f)
best_candidate[b] = global_best_c;
else
best_candidate[b] = best_c;
}
}
}
free(history);
/* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* Phase 3.5: Born-Rule Multi-Shot Scale Refinement
*
* The beam search found the MAP candidate sequence. But the
* triality marginals encode quantum phase-coherent structure
* that a greedy beam can miss.
*
* Like tesseract_factor's MCMC period recovery (lines 1920-1964):
* 1. Take N independent Born samples from triality marginals
* 2. Each sample β full candidate assignment across all blocks
* 3. Evaluate actual RMSE for each assignment
* 4. Keep assignment with lowest total RMSE
*
* Reuses the EXISTING converged MΓΆbius sheet β zero new BP.
* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
{
#define Q4_BORN_SHOTS 64
/* Compute beam-search baseline RMSE for comparison */
float beam_total_err = 0.0f;
for (int64_t bi = 0; bi < n_blocks; bi++)
beam_total_err += cand_errors[bi][best_candidate[bi]];
/* Build per-block CDFs from triality marginals */
unsigned int born_rng = 314159;
int *shot_assignment = (int *)malloc(n_blocks * sizeof(int));
for (int shot = 0; shot < Q4_BORN_SHOTS; shot++) {
float shot_err = 0.0f;
/* Init from beam result so tail blocks beyond
* graph_blocks*stride keep valid indices */
memcpy(shot_assignment, best_candidate,
n_blocks * sizeof(int));
for (int64_t gi = 0; gi < graph_blocks; gi++) {
/* Normalize marginals to CDF */
double m_total = 0.0;
for (int v = 0; v < 6; v++) m_total += marg[gi][v];
/* Born sample: CDF inversion (same as born_sample) */
born_rng = born_rng * 1664525u + 1013904223u;
double rnd = (double)(born_rng >> 8) / 16777216.0;
double target = rnd * m_total;
double cum = 0.0;
int sampled_qi = 5;
for (int v = 0; v < 6; v++) {
cum += marg[gi][v];
if (cum > target) { sampled_qi = v; break; }
}
/* Find the best candidate WITHIN this quhit bin */
int64_t blk = gi * stride;
float best_bin_err = 1e30f;
int best_bin_cand = 10; /* default */
for (int ci = 0; ci < Q4_N_CAND; ci++) {
if (Q4_CAND_TO_QUHIT[ci] == sampled_qi) {
if (cand_errors[blk][ci] < best_bin_err) {
best_bin_err = cand_errors[blk][ci];
best_bin_cand = ci;
}
}
}
shot_assignment[blk] = best_bin_cand;
shot_err += cand_errors[blk][best_bin_cand];
}
/* Metropolis acceptance: adopt if better than current best */
if (shot_err < beam_total_err) {
for (int64_t b = 0; b < n_blocks; b++)
best_candidate[b] = shot_assignment[b];
beam_total_err = shot_err;
}
}
free(shot_assignment);
}
free(marg);
hpc_destroy(graph);
}
}
/* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* PHASE 4: Assemble blocks via least-squares scale extraction
*
* The factorer assembles a frequency register from BP marginals,
* then EXTRACTS the exact period via continued fractions.
*
* We do the same: the beam search / Born shots selected a grid
* candidate (the "assembled frequency"). Now we EXTRACT the exact
* optimal FP16 scale via weighted least-squares (the "CF step").
*
* For Q4_0: d_optimal = Ξ£(w_j Γ x_j Γ qΜ_j) / Ξ£(w_j Γ qΜ_jΒ²)
* where qΜ_j = (q_j - 8) and q_j is quantized at the grid scale.
*
* This iterates: quantize at d_init β compute d_optimal β re-quantize
* β re-compute until convergence. 3 iterations suffice since Q4_0
* has only 16 levels β the assignment stabilizes immediately.
*
* The grid gave us 16 possible scales. This gives us 65,536 (all FP16).
* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
#pragma omp parallel for schedule(dynamic, 64) reduction(+:total_err)
for (int64_t blk = 0; blk < n_blocks; blk++) {
const float *bw = weights + blk * QK4_0;
int cidx = best_candidate[blk];
/* Start from the grid-selected scale (the "assembled frequency") */
float d_current = gguf_fp16_to_fp32(cand_d16[blk][cidx]);
/* Analog assembly: iterate to full convergence.
* 5 iterations for stable (d, q-values) coupling. */
for (int ls_iter = 0; ls_iter < 5; ls_iter++) {
if (d_current < 1e-15f) break;
float id = 1.0f / d_current;
/* Quantize at current scale */
int qs_tmp[QK4_0];
for (int j = 0; j < QK4_0; j++) {
int q = (int)(bw[j] * id + 8.5f);
if (q < 0) q = 0; if (q > 15) q = 15;
qs_tmp[j] = q;
}
/* Weighted least-squares: d = Ξ£(w Γ x Γ qΜ) / Ξ£(w Γ qΜΒ²)
* where qΜ = q - 8 (centered quantized value) */
float num = 0.0f, den = 0.0f;
for (int j = 0; j < QK4_0; j++) {
float q_centered = (float)qs_tmp[j] - 8.0f;
float w = (imat_importance) ?
imat_importance[blk * QK4_0 + j] : 1.0f;
num += w * bw[j] * q_centered;
den += w * q_centered * q_centered;
}
if (den > 1e-15f) {
float d_new = num / den;
/* Clamp magnitude to prevent runaway (Q4_0 d can be negative) */
float d_seed = gguf_fp16_to_fp32(cand_d16[blk][cidx]);
if (fabsf(d_new) < 4.0f * (fabsf(d_seed) + 1e-10f)) {
uint16_t d16 = gguf_fp32_to_fp16(d_new);
d_current = gguf_fp16_to_fp32(d16);
}
}
}
/* ββ FP16 ULP neighborhood search + sign-flip exploration ββ
* The WLS solve found the continuous-optimal d. But FP16 truncation
* may shift the optimum. Try Β±4 ULP around d in FP16 space, plus
* the negated scale, and pick the one with minimum reconstruction error. */
{
uint16_t base_d16 = gguf_fp32_to_fp16(d_current);
uint16_t best_d16 = base_d16;
float best_ulp_err = 1e30f;
/* Try Β±4 ULP neighborhood + sign flip = up to 17 candidates */
uint16_t ulp_candidates[17];
int n_ulp = 0;
for (int delta = -4; delta <= 4; delta++) {
int cand16 = (int)base_d16 + delta;
if (cand16 >= 0 && cand16 <= 0x7BFF) /* valid positive FP16 */
ulp_candidates[n_ulp++] = (uint16_t)cand16;
}
/* Sign-flipped d: negate and try Β±0 ULP */
{
float neg_d = -d_current;
uint16_t neg_d16 = gguf_fp32_to_fp16(neg_d);
ulp_candidates[n_ulp++] = neg_d16;
}
for (int ui = 0; ui < n_ulp; ui++) {
float trial_d = gguf_fp16_to_fp32(ulp_candidates[ui]);
float trial_id = (fabsf(trial_d) > 1e-15f) ? 1.0f / trial_d : 0.0f;
float err = 0.0f;
for (int j = 0; j < QK4_0; j++) {
int q = (int)(bw[j] * trial_id + 8.5f);
if (q < 0) q = 0; if (q > 15) q = 15;
float deq = ((float)q - 8.0f) * trial_d;
float w = (imat_importance) ? imat_importance[blk * QK4_0 + j] : 1.0f;
err += (bw[j] - deq) * (bw[j] - deq) * w;
}
if (err < best_ulp_err) {
best_ulp_err = err;
best_d16 = ulp_candidates[ui];
}
}
d_current = gguf_fp16_to_fp32(best_d16);
}
/* Store the extracted optimal FP16 scale */
output[blk].d = gguf_fp32_to_fp16(d_current);
float actual_d = d_current;
float id = (fabsf(actual_d) > 1e-15f) ? 1.0f / actual_d : 0.0f;
/* ββ Dβ Hadamard Error Shaping for Q4_0 ββ
* 32 elements per block = 5 full Dβ groups of 6 + 2 tail.
* Apply the same antipodal fold as Q2_K: minimize vesica energy
* to push quantization noise into wave (high-frequency) modes
* that cancel in dot products. */
/* Step 1: Standard nearest-rounding as baseline */
int q_base[QK4_0], q_shaped[QK4_0];
float q_cont[QK4_0];
for (int j = 0; j < QK4_0; j++) {
q_cont[j] = bw[j] * id + 8.0f;
q_base[j] = (int)(q_cont[j] + 0.5f);
if (q_base[j] < 0) q_base[j] = 0;
if (q_base[j] > 15) q_base[j] = 15;
}
memcpy(q_shaped, q_base, QK4_0 * sizeof(int));
/* Step 2: Dβ greedy flipping on 5 groups of 6 */
for (int g = 0; g < 5; g++) {
int g_off = g * 6;
for (int pass = 0; pass < 6; pass++) {
int best_k = -1;
int best_q_alt = 0;
float best_delta = 0.0f;
/* Current group errors */
float e_cur[6];
for (int kk = 0; kk < 6; kk++) {
float deq = ((float)q_shaped[g_off+kk] - 8.0f) * actual_d;
e_cur[kk] = bw[g_off+kk] - deq;
}
/* Current Dβ metric: vesica energy + DCΒ² */
float vesica_cur = 0.0f, dc_cur = 0.0f;
for (int p = 0; p < 3; p++) {
float v = e_cur[p] + e_cur[p+3];
vesica_cur += v * v;
}
for (int kk = 0; kk < 6; kk++) dc_cur += e_cur[kk];
float metric_cur = 4.0f * vesica_cur + dc_cur * dc_cur;
/* Try flipping each element */
for (int k = 0; k < 6; k++) {
int idx = g_off + k;
int q_cur = q_shaped[idx];
int q_try;
if (q_cont[idx] - (float)q_cur >= 0) {
q_try = q_cur + 1;
} else {
q_try = q_cur - 1;
}
if (q_try < 0 || q_try > 15) continue;
/* Alt errors */
float e_alt[6];
for (int kk = 0; kk < 6; kk++) e_alt[kk] = e_cur[kk];
float deq_try = ((float)q_try - 8.0f) * actual_d;
e_alt[k] = bw[idx] - deq_try;
/* Alt Dβ metric */
float vesica_alt = 0.0f, dc_alt = 0.0f;
for (int p = 0; p < 3; p++) {
float v = e_alt[p] + e_alt[p+3];
vesica_alt += v * v;
}
for (int kk = 0; kk < 6; kk++) dc_alt += e_alt[kk];
float metric_alt = 4.0f * vesica_alt + dc_alt * dc_alt;
float delta = metric_cur - metric_alt;
if (delta > best_delta) {
best_delta = delta;
best_k = k;
best_q_alt = q_try;
}
}
if (best_k < 0) break;
q_shaped[g_off + best_k] = best_q_alt;
}
}
/* Step 3: Error comparison β keep shaped only if MSE doesn't worsen >5% */
float err_base = 0.0f, err_shaped = 0.0f;
for (int j = 0; j < QK4_0; j++) {
float w = (imat_importance) ? imat_importance[blk * QK4_0 + j] : 1.0f;
float deq_b = ((float)q_base[j] - 8.0f) * actual_d;
float deq_s = ((float)q_shaped[j] - 8.0f) * actual_d;
err_base += (bw[j] - deq_b) * (bw[j] - deq_b) * w;
err_shaped += (bw[j] - deq_s) * (bw[j] - deq_s) * w;
}
int *q_final = (err_shaped <= err_base * 1.05f) ? q_shaped : q_base;
/* Pack nibbles and compute error */
for (int j = 0; j < QK4_0 / 2; j++) {
int q0 = q_final[j];
int q1 = q_final[j + QK4_0/2];
output[blk].qs[j] = (uint8_t)(q0 | (q1 << 4));
float deq0 = ((float)q0 - 8.0f) * actual_d;
float deq1 = ((float)q1 - 8.0f) * actual_d;
total_err += (bw[j] - deq0) * (bw[j] - deq0) + (bw[j + QK4_0/2] - deq1) * (bw[j + QK4_0/2] - deq1);
}
}
*out_total_error = total_err;
free(greedy_d);
free(cand_errors);
free(cand_d16);
free(best_candidate);
}
static void quantize_tensor_q2k_hpc(const float *weights, int64_t n_elements,
BlockQ2K *output, float *out_total_error,
OptimizerMode opt_mode,
const float *imat_importance,
int verbose)
{
int64_t n_blocks = n_elements / QK_K;
float total_err = 0.0f;
const int N_SUB = QK_K / 16;
init_scale_table();
/* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* PHASE 1: Greedy quantization β produce seed (d, dmin) per block
* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
/* Store Phase A/B results for all blocks */
typedef struct {
float dm, mm; /* greedy d, dmin (fp32) */
uint16_t d_fp16, dmin_fp16; /* greedy d, dmin (fp16) */
uint8_t Ls[16], Lm[16]; /* sub-block scale/min indices */
float scales[16], mins[16], sw[16];
} BlockSeed;
BlockSeed *seeds = (BlockSeed *)calloc(n_blocks, sizeof(BlockSeed));
#pragma omp parallel for schedule(dynamic, 64)
for (int64_t blk = 0; blk < n_blocks; blk++) {
const float *block_x = weights + blk * QK_K;
uint8_t L[QK_K], Laux[16];
float wt[16];
float sumx2 = 0;
for (int i = 0; i < QK_K; i++) sumx2 += block_x[i] * block_x[i];
float sigma2 = sumx2 / (float)QK_K;
for (int j = 0; j < N_SUB; j++) {
const float *sx = block_x + 16 * j;
seeds[blk].sw[j] = 0;
for (int l = 0; l < 16; l++) {
float imp = (imat_importance) ? imat_importance[blk * QK_K + 16 * j + l] : 1.0f;
wt[l] = imp * sqrtf(sigma2 + sx[l] * sx[l]);
seeds[blk].sw[j] += wt[l];
}
seeds[blk].scales[j] = hpc_make_qkx2_quants(16, 3, sx, wt,
L + 16 * j, &seeds[blk].mins[j], Laux);
}
seeds[blk].dm = hpc_make_qp_quants(N_SUB, 15, seeds[blk].scales,
seeds[blk].Ls, seeds[blk].sw);
seeds[blk].mm = hpc_make_qp_quants(N_SUB, 15, seeds[blk].mins,
seeds[blk].Lm, seeds[blk].sw);
seeds[blk].d_fp16 = gguf_fp32_to_fp16(seeds[blk].dm);
seeds[blk].dmin_fp16 = gguf_fp32_to_fp16(seeds[blk].mm);
}
/* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* PHASE 2: WLS-Optimal Candidate Generation
*
* Instead of a fixed multiplier grid centered on greedy seeds,
* we first solve a 3-iteration Weighted Least-Squares to find
* the true optimal (d*, dmin*) per block, then generate the
* 16Γ16 candidate grid centered on THOSE optimal values.
* This makes the candidate space data-driven, not fabricated.
* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
/* Wide neighborhood around WLS optimum: Β±20% with asymmetric spacing
* β finer near 1.0 for precision, wider at edges for exploration.
* Critical for large-Ο weights where the optimal (d,dmin) may be
* far from the WLS seed. */
static const float NEIGHBOR_MULTS_D[N_CAND_D] = {
0.800f, 0.850f, 0.890f, 0.920f, 0.945f, 0.965f, 0.980f, 0.990f,
1.010f, 1.020f, 1.035f, 1.055f, 1.080f, 1.110f, 1.150f, 1.200f
};
static const float NEIGHBOR_MULTS_M[N_CAND_M] = {
0.800f, 0.850f, 0.890f, 0.920f, 0.945f, 0.965f, 0.980f, 0.990f,
1.010f, 1.020f, 1.035f, 1.055f, 1.080f, 1.110f, 1.150f, 1.200f
};
/* Map 16 candidates β 6 quhit states for BP encoding */
static const int CAND_TO_QUHIT[16] = {
0, 0, 0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5
};
/* candidate_errors[blk][256] β weighted MSE per candidate */
float (*candidate_errors)[TOTAL_SCALE_CANDIDATES] = NULL;
uint16_t (*candidate_d)[TOTAL_SCALE_CANDIDATES] = NULL;
uint16_t (*candidate_dmin)[TOTAL_SCALE_CANDIDATES] = NULL;
/* Per-candidate Ls/Lm β must recompute for each (d, dmin) */
uint8_t (*candidate_Ls)[TOTAL_SCALE_CANDIDATES][16] = NULL;
uint8_t (*candidate_Lm)[TOTAL_SCALE_CANDIDATES][16] = NULL;
candidate_errors = (float (*)[TOTAL_SCALE_CANDIDATES])calloc(n_blocks,
sizeof(float[TOTAL_SCALE_CANDIDATES]));
candidate_d = (uint16_t (*)[TOTAL_SCALE_CANDIDATES])calloc(n_blocks,
sizeof(uint16_t[TOTAL_SCALE_CANDIDATES]));
candidate_dmin = (uint16_t (*)[TOTAL_SCALE_CANDIDATES])calloc(n_blocks,
sizeof(uint16_t[TOTAL_SCALE_CANDIDATES]));
candidate_Ls = (uint8_t (*)[TOTAL_SCALE_CANDIDATES][16])calloc(n_blocks,
sizeof(uint8_t[TOTAL_SCALE_CANDIDATES][16]));
candidate_Lm = (uint8_t (*)[TOTAL_SCALE_CANDIDATES][16])calloc(n_blocks,
sizeof(uint8_t[TOTAL_SCALE_CANDIDATES][16]));
#pragma omp parallel for schedule(dynamic, 16)
for (int64_t blk = 0; blk < n_blocks; blk++) {
const float *block_x = weights + blk * QK_K;
/* ββ Step 2a: WLS solve to find optimal (d*, dmin*) ββ
* Seed from Phase 1 greedy, iterate 3Γ to converge.
* Q2_K model: x[j,k] β d Γ Ls[j] Γ q[j,k] - dmin Γ Lm[j]
* This is a 2-variable WLS: minimize Ξ£ wΓ(x - dΓa + dminΓb)Β² */
float wls_dm = seeds[blk].dm;
float wls_mm = seeds[blk].mm;
uint8_t wls_Ls[16], wls_Lm[16];
memcpy(wls_Ls, seeds[blk].Ls, 16);
memcpy(wls_Lm, seeds[blk].Lm, 16);
for (int ls_iter = 0; ls_iter < 5; ls_iter++) {
/* Quantize all elements at current (wls_dm, wls_mm) */
uint8_t L_wls[QK_K];
for (int j = 0; j < N_SUB; j++) {
float d_sub = wls_dm * (float)wls_Ls[j];
float m_sub = wls_mm * (float)wls_Lm[j];
if (d_sub < 1e-15f) {
for (int k = 0; k < 16; k++) L_wls[16*j+k] = 0;
continue;
}
for (int k = 0; k < 16; k++) {
int q = gguf_nearest_int((block_x[16*j+k] + m_sub) / d_sub);
if (q < 0) q = 0; if (q > 3) q = 3;
L_wls[16*j+k] = (uint8_t)q;
}
}
/* Accumulate 2Γ2 normal equations */
double Saa = 0, Sab = 0, Sbb = 0, Sxa = 0, Sxb = 0;
for (int j = 0; j < N_SUB; j++) {
float ls_f = (float)wls_Ls[j];
float lm_f = (float)wls_Lm[j];
for (int k = 0; k < 16; k++) {
float x = block_x[16*j+k];
float w = (imat_importance) ?
imat_importance[blk * QK_K + 16*j+k] : 1.0f;
float a = ls_f * (float)L_wls[16*j+k];
float b = lm_f;
Saa += w * a * a;
Sab += w * a * b;
Sbb += w * b * b;
Sxa += w * x * a;
Sxb += w * x * b;
}
}
/* Solve via Cramer's rule */
double det = Saa * Sbb - Sab * Sab;
if (fabs(det) > 1e-30) {
double d_new = (Sbb * Sxa - Sab * Sxb) / det;
double dm_new = (Sab * Sxa - Saa * Sxb) / det;
/* Clamp: positive and within 4Γ of seed (prevent runaway) */
if (d_new > 0.0 && d_new < 4.0 * (seeds[blk].dm + 1e-10))
wls_dm = gguf_fp16_to_fp32(gguf_fp32_to_fp16((float)d_new));
if (dm_new > 0.0 && dm_new < 4.0 * (seeds[blk].mm + 1e-10))
wls_mm = gguf_fp16_to_fp32(gguf_fp32_to_fp16((float)dm_new));
}
/* Re-derive Ls/Lm for updated (d*, dmin*) */
for (int j = 0; j < N_SUB; j++) {
if (wls_dm > 1e-15f) {
int ls = gguf_nearest_int(seeds[blk].scales[j] / wls_dm);
if (ls < 0) ls = 0; if (ls > 15) ls = 15;
wls_Ls[j] = (uint8_t)ls;
} else { wls_Ls[j] = 0; }
if (wls_mm > 1e-15f) {
int lm = gguf_nearest_int(seeds[blk].mins[j] / wls_mm);
if (lm < 0) lm = 0; if (lm > 15) lm = 15;
wls_Lm[j] = (uint8_t)lm;
} else { wls_Lm[j] = 0; }
}
}
/* ββ Step 2b: Generate 16Γ16 candidates centered on WLS optimum ββ
* Grid is now centered on (wls_dm, wls_mm) not (greedy_dm, greedy_mm).
* Tighter spacing because we're already near the true minimum. */
for (int di = 0; di < N_CAND_D; di++) {
float trial_dm = wls_dm * NEIGHBOR_MULTS_D[di];
uint16_t trial_d16 = gguf_fp32_to_fp16(trial_dm);
float actual_dm = gguf_fp16_to_fp32(trial_d16);
for (int mi = 0; mi < N_CAND_M; mi++) {
int cidx = di * N_CAND_M + mi;
float trial_mm = wls_mm * NEIGHBOR_MULTS_M[mi];
uint16_t trial_dmin16 = gguf_fp32_to_fp16(trial_mm);
float actual_mm = gguf_fp16_to_fp32(trial_dmin16);
candidate_d[blk][cidx] = trial_d16;
candidate_dmin[blk][cidx] = trial_dmin16;
/* Recompute Ls/Lm for THIS candidate dm/mm */
uint8_t trial_Ls[16], trial_Lm[16];
for (int j = 0; j < N_SUB; j++) {
if (actual_dm > 1e-15f) {
int ls = gguf_nearest_int(seeds[blk].scales[j] / actual_dm);
if (ls < 0) ls = 0; if (ls > 15) ls = 15;
trial_Ls[j] = (uint8_t)ls;
} else {
trial_Ls[j] = 0;
}
if (actual_mm > 1e-15f) {
int lm = gguf_nearest_int(seeds[blk].mins[j] / actual_mm);
if (lm < 0) lm = 0; if (lm > 15) lm = 15;
trial_Lm[j] = (uint8_t)lm;
} else {
trial_Lm[j] = 0;
}
}
memcpy(candidate_Ls[blk][cidx], trial_Ls, 16);
memcpy(candidate_Lm[blk][cidx], trial_Lm, 16);
/* Fully re-quantize and measure weighted MSE */
float err = 0.0f;
for (int j = 0; j < N_SUB; j++) {
float d = actual_dm * (float)trial_Ls[j];
float m = actual_mm * (float)trial_Lm[j];
if (d < 1e-15f) {
for (int k = 0; k < 16; k++) {
float x = block_x[16 * j + k];
float w = (imat_importance) ?
imat_importance[blk * QK_K + 16 * j + k] : 1.0f;
err += x * x * w;
}
continue;
}
for (int k = 0; k < 16; k += 6) {
int g_len = (k + 6 <= 16) ? 6 : (16 - k);
int half_g = g_len / 2;
float e_cur[6], w_cur[6];
for (int kk = 0; kk < g_len; kk++) {
int idx = 16 * j + k + kk;
float x = block_x[idx];
int q = gguf_nearest_int((x + m) / d);
if (q < 0) q = 0; if (q > 3) q = 3;
float deq = d * (float)q - m;
e_cur[kk] = x - deq;
w_cur[kk] = (imat_importance) ? imat_importance[blk * QK_K + idx] : 1.0f;
}
/* Decompose into vesica and wave */
float vesica_err = 0.0f, wave_err = 0.0f;
for (int p = 0; p < half_g; p++) {
float v = e_cur[p] + e_cur[p + half_g];
float w_wave = e_cur[p] - e_cur[p + half_g];
float w_avg = (w_cur[p] + w_cur[p + half_g]) * 0.5f;
vesica_err += v * v * w_avg;
wave_err += w_wave * w_wave * w_avg;
}
/* Triality weighting: penalize vesica 4Γ, wave 1Γ */
err += 0.5f * (4.0f * vesica_err + 1.0f * wave_err);
}
}
candidate_errors[blk][cidx] = err;
}
}
}
/* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* PHASE 3: HPC Graph β Shor's Griffiths-Niu Measurement
*
* Build a multi-quhit graph where each block has 2 quhits
* encoding the 36 candidate errors. Shor's sequential measurement
* (IDFT6 + feed-forward + collapse/back-action) extracts exact
* marginals for optimal (d, dmin) per block β replaces BP.
* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
/* Default: use greedy candidate (index 5*10+5 = 55, mult 1.00Γ1.00) */
int *best_candidate = (int *)malloc(n_blocks * sizeof(int));
for (int64_t i = 0; i < n_blocks; i++)
best_candidate[i] = 10 * N_CAND_M + 10; /* NEIGHBOR_MULTS_D[10]=1.00, _M[10]=1.00 */
if (opt_mode != OPT_MSE && n_blocks >= 2) {
int64_t graph_blocks = (n_blocks > 2000) ? 2000 : n_blocks;
int64_t stride = n_blocks / graph_blocks;
float temperature = 0.5f;
int64_t n_sites = graph_blocks * QUHITS_PER_BLOCK;
HPCGraph *graph = hpc_create(n_sites);
if (graph) {
for (int64_t i = 0; i < n_sites; i++)
triality_dft(&graph->locals[i]);
/* Encode each stride group's AGGREGATED candidate errors as dual-quhit
* amplitudes. For stride > 1, average errors across ALL blocks in
* the group β not just the first block. This is critical for large
* tensors where stride=97 means 96/97 blocks were being ignored. */
/* Compute adaptive temperature from median error spread.
* This ensures the Boltzmann encoding produces meaningful distributions
* regardless of weight magnitude (Ο=0.0003 vs Ο=0.024). */
{
double err_accum = 0.0;
int err_count = 0;
for (int64_t gi = 0; gi < graph_blocks && gi < 100; gi++) {
int64_t blk = gi * stride;
float max_e = 0.0f;
for (int c = 0; c < TOTAL_SCALE_CANDIDATES; c++)
if (candidate_errors[blk][c] > max_e)
max_e = candidate_errors[blk][c];
err_accum += (double)max_e;
err_count++;
}
if (err_count > 0) {
float median_err = (float)(err_accum / err_count);
/* Temperature = 10% of median max error β sharp enough to
* discriminate, soft enough for Shor interference */
temperature = median_err * 0.1f;
if (temperature < 1e-10f) temperature = 1e-10f;
}
}
for (int64_t i = 0; i < graph_blocks; i++) {
/* Aggregate errors across entire stride group */
float agg_errors[TOTAL_SCALE_CANDIDATES];
for (int c = 0; c < TOTAL_SCALE_CANDIDATES; c++)
agg_errors[c] = 0.0f;
int64_t blk_start = i * stride;
int64_t blk_end = blk_start + stride;
if (blk_end > n_blocks) blk_end = n_blocks;
int64_t group_size = blk_end - blk_start;
for (int64_t b = blk_start; b < blk_end; b++) {
for (int c = 0; c < TOTAL_SCALE_CANDIDATES; c++)
agg_errors[c] += candidate_errors[b][c];
}
/* Average across group */
if (group_size > 1) {
float inv_gs = 1.0f / (float)group_size;
for (int c = 0; c < TOTAL_SCALE_CANDIDATES; c++)
agg_errors[c] *= inv_gs;
}
float min_err = 1e30f;
for (int c = 0; c < TOTAL_SCALE_CANDIDATES; c++)
if (agg_errors[c] < min_err)
min_err = agg_errors[c];
/* Quhit 0 (coarse = d dimension): marginalize over dmin */
double coarse_re[6];
double coarse_norm = 0.0;
for (int qi = 0; qi < 6; qi++) coarse_re[qi] = 0.0;
for (int di = 0; di < N_CAND_D; di++) {
int qi = CAND_TO_QUHIT[di];
for (int mi = 0; mi < N_CAND_M; mi++) {
int cidx = di * N_CAND_M + mi;
coarse_re[qi] += exp(-(double)(agg_errors[cidx] - min_err) /
(2.0 * (double)temperature));
}
}
for (int qi = 0; qi < 6; qi++)
coarse_norm += coarse_re[qi] * coarse_re[qi];
if (coarse_norm > 1e-30) {
double inv = 1.0 / sqrt(coarse_norm);
for (int v = 0; v < 6; v++) coarse_re[v] *= inv;
}
/* Quhit 1 (fine = dmin dimension): marginalize over d */
double fine_re[6];
double fine_norm = 0.0;
for (int qi = 0; qi < 6; qi++) fine_re[qi] = 0.0;
for (int mi = 0; mi < N_CAND_M; mi++) {
int qi = CAND_TO_QUHIT[mi];
for (int di = 0; di < N_CAND_D; di++) {
int cidx = di * N_CAND_M + mi;
fine_re[qi] += exp(-(double)(agg_errors[cidx] - min_err) /
(2.0 * (double)temperature));
}
}
for (int qi = 0; qi < 6; qi++)
fine_norm += fine_re[qi] * fine_re[qi];
if (fine_norm > 1e-30) {
double inv = 1.0 / sqrt(fine_norm);
for (int v = 0; v < 6; v++) fine_re[v] *= inv;
}
/* Write quhits */
int64_t s0 = 2 * i, s1 = 2 * i + 1;
for (int v = 0; v < 6; v++) {
graph->locals[s0].edge_re[v] = coarse_re[v];
graph->locals[s0].edge_im[v] = 0.0;
graph->locals[s1].edge_re[v] = fine_re[v];
graph->locals[s1].edge_im[v] = 0.0;
}
graph->locals[s0].primary = VIEW_EDGE;
graph->locals[s0].dirty = DIRTY_VERTEX | DIRTY_DIAGONAL | DIRTY_FOLDED;
graph->locals[s0].delta_valid = 0;
triality_update_mask(&graph->locals[s0]);
graph->locals[s1].primary = VIEW_EDGE;
graph->locals[s1].dirty = DIRTY_VERTEX | DIRTY_DIAGONAL | DIRTY_FOLDED;
graph->locals[s1].delta_valid = 0;
triality_update_mask(&graph->locals[s1]);
}
/* Build edges */
for (int64_t i = 0; i < graph_blocks; i++) {
hpc_cz(graph, 2 * i, 2 * i + 1); /* intra-block: d β dmin */
if (i + 1 < graph_blocks) {
hpc_cz(graph, 2 * i, 2 * (i + 1)); /* d β d neighbor */
hpc_cz(graph, 2 * i + 1, 2 * (i + 1) + 1); /* dmin β dmin */
}
}
/* ββ Shor's Griffiths-Niu Sequential Measurement (dual quhit) ββ
* Replaces BP with exact marginals via IDFT6 + feed-forward +
* collapse/back-action (ported 1:1 from tesseract_factor.c).
*
* The dual-quhit graph has 2Γgraph_blocks sites:
* Even sites (s0 = 2*i): coarse (d dimension)
* Odd sites (s1 = 2*i+1): fine (dmin dimension)
*
* Single-pass sequential measurement produces exact marginals
* for both dimensions simultaneously through the CZ correlations. */
double (*shor_marg)[6] = (double (*)[6])calloc(n_sites, sizeof(double[6]));
int *shor_measured = (int *)calloc(n_sites, sizeof(int));
shor_measure_graph(graph, n_sites, shor_marg, shor_measured, 1);
/* Extract coarse (d) and fine (dmin) marginals from Shor output */
double (*coarse_marg)[6] = (double (*)[6])calloc(graph_blocks, sizeof(double[6]));
double (*fine_marg)[6] = (double (*)[6])calloc(graph_blocks, sizeof(double[6]));
for (int64_t i = 0; i < graph_blocks; i++) {
for (int v = 0; v < 6; v++) {
coarse_marg[i][v] = shor_marg[2 * i][v];
fine_marg[i][v] = shor_marg[2 * i + 1][v];
}
}
free(shor_marg);
free(shor_measured);
/* ββ Hensel-Inspired Beam Search Constraint Propagation ββ
* Like tesseract_factor's Hensel lift: process blocks sequentially,
* maintain K best configurations, prune by accumulated error.
*
* The constraint: blocks are selected JOINTLY. */
#define N_BEAMS 24 /* K beams β widened for 31B (was 12) */
typedef struct {
double acc_error;
int history_idx; /* index into the backpointer array */
} QuantBeam;
typedef struct {
int cand_idx;
int parent_idx;
} BeamHistory;
QuantBeam beams[N_BEAMS];
int active_beams = 1;
/* Pre-allocate history to avoid O(N^2) memory copies */
BeamHistory *history = (BeamHistory *)malloc(n_blocks * N_BEAMS * sizeof(BeamHistory));
for (int b = 0; b < N_BEAMS; b++) {
beams[b].acc_error = 0.0;
beams[b].history_idx = -1;
}
/* Process blocks sequentially with beam search */
for (int64_t i = 0; i < graph_blocks; i++) {
double c_total = 0.0, f_total = 0.0;
for (int v = 0; v < 6; v++) {
c_total += coarse_marg[i][v];
f_total += fine_marg[i][v];
}
/* Candidate scores for this block: triality prob Γ (1/normalized_error) */
double cand_score[TOTAL_SCALE_CANDIDATES];
int64_t blk = i * stride;
int d_bin_count[6] = {0}, m_bin_count[6] = {0};
for (int k = 0; k < N_CAND_D; k++) d_bin_count[CAND_TO_QUHIT[k]]++;
for (int k = 0; k < N_CAND_M; k++) m_bin_count[CAND_TO_QUHIT[k]]++;
/* Per-block error normalization: divide by block mean error
* so small-weight blocks don't dominate beam selection */
float blk_mean_err = 0.0f;
for (int c = 0; c < TOTAL_SCALE_CANDIDATES; c++)
blk_mean_err += candidate_errors[blk][c];
blk_mean_err /= (float)TOTAL_SCALE_CANDIDATES;
if (blk_mean_err < 1e-30f) blk_mean_err = 1e-30f;
for (int di = 0; di < N_CAND_D; di++) {
int qi_d = CAND_TO_QUHIT[di];
double p_d = (c_total > 1e-30) ? coarse_marg[i][qi_d] / c_total : 1.0/6.0;
p_d /= (double)d_bin_count[qi_d];
for (int mi = 0; mi < N_CAND_M; mi++) {
int qi_m = CAND_TO_QUHIT[mi];
double p_m = (f_total > 1e-30) ? fine_marg[i][qi_m] / f_total : 1.0/6.0;
p_m /= (double)m_bin_count[qi_m];
int cidx = di * N_CAND_M + mi;
cand_score[cidx] = p_d * p_m / (candidate_errors[blk][cidx] / blk_mean_err + 1e-15);
}
}
/* Extend beams Γ 36 candidates, keep top K */
typedef struct { double score; int beam_idx; int cand_idx; } BeamExt;
BeamExt extensions[N_BEAMS * TOTAL_SCALE_CANDIDATES];
int n_ext = 0;
for (int b = 0; b < active_beams; b++) {
for (int c = 0; c < TOTAL_SCALE_CANDIDATES; c++) {
/* Score = -(accumulated_error + this_block_error) Γ triality_prob */
double ext_err = beams[b].acc_error + candidate_errors[blk][c];
double ext_score = cand_score[c] / (ext_err + 1e-15);
extensions[n_ext].score = ext_score;
extensions[n_ext].beam_idx = b;
extensions[n_ext].cand_idx = c;
n_ext++;
}
}
/* Top-K selection */
int top_k = (n_ext < N_BEAMS) ? n_ext : N_BEAMS;
int top_indices[N_BEAMS];
for (int k = 0; k < top_k; k++) {
int best = -1;
double best_s = -1e30;
for (int e = 0; e < n_ext; e++) {
if (extensions[e].score > best_s) {
best_s = extensions[e].score;
best = e;
}
}
top_indices[k] = best;
extensions[best].score = -2e30; /* poison */
}
/* Build new beams from top-K extensions using backpointers */
QuantBeam new_beams[N_BEAMS];
for (int k = 0; k < top_k; k++) {
int ext_idx = top_indices[k];
int src_beam = extensions[ext_idx].beam_idx;
int cand = extensions[ext_idx].cand_idx;
int hist_idx = i * N_BEAMS + k;
history[hist_idx].cand_idx = cand;
history[hist_idx].parent_idx = beams[src_beam].history_idx;
new_beams[k].history_idx = hist_idx;
new_beams[k].acc_error = beams[src_beam].acc_error
+ candidate_errors[blk][cand];
}
for (int k = 0; k < top_k; k++)
beams[k] = new_beams[k];
active_beams = top_k;
}
/* Trace back the best beam's selections.
* The beam search selects one candidate per GRAPH NODE (stride group).
* For stride > 1, each block within the stride group independently
* picks its own best candidate β using the beam's coarse/fine quhit
* bins as a constraint, but evaluating its own candidate_errors.
* This eliminates stride-aliasing: previously 96/97 blocks were
* forced to use a candidate chosen for 1 representative block. */
int curr_hist = beams[0].history_idx;
for (int64_t i = graph_blocks - 1; i >= 0; i--) {
int group_cidx;
if (curr_hist >= 0) {
group_cidx = history[curr_hist].cand_idx;
curr_hist = history[curr_hist].parent_idx;
} else {
group_cidx = 10 * N_CAND_M + 10;
}
if (stride <= 1) {
/* No stride group β direct assignment */
best_candidate[i] = group_cidx;
} else {
/* Per-block local optimization within the stride group.
* The beam-selected candidate determines the target quhit
* bins (d_bin, dmin_bin). Each block picks its own best
* candidate that falls in compatible bins, or falls back
* to the globally best candidate for that block. */
int group_di = group_cidx / N_CAND_M;
int group_mi = group_cidx % N_CAND_M;
int target_d_bin = CAND_TO_QUHIT[group_di];
int target_m_bin = CAND_TO_QUHIT[group_mi];
for (int64_t b = i * stride; b < (i+1) * stride && b < n_blocks; b++) {
/* Find best candidate in same quhit bins */
float best_err = 1e30f;
int best_c = group_cidx;
for (int di = 0; di < N_CAND_D; di++) {
if (CAND_TO_QUHIT[di] != target_d_bin) continue;
for (int mi = 0; mi < N_CAND_M; mi++) {
if (CAND_TO_QUHIT[mi] != target_m_bin) continue;
int cidx = di * N_CAND_M + mi;
if (candidate_errors[b][cidx] < best_err) {
best_err = candidate_errors[b][cidx];
best_c = cidx;
}
}
}
/* Also check if the block's overall best is significantly
* better β if so, use it (greedy override) */
float global_best = 1e30f;
int global_best_c = group_cidx;
for (int c = 0; c < TOTAL_SCALE_CANDIDATES; c++) {
if (candidate_errors[b][c] < global_best) {
global_best = candidate_errors[b][c];
global_best_c = c;
}
}
/* Use bin-constrained choice unless the global best
* is >5% better β preserves Shor coherence while
* allowing escape from bad bin assignments */
if (global_best < best_err * 0.95f)
best_candidate[b] = global_best_c;
else
best_candidate[b] = best_c;
}
}
}
free(history);
/* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* Phase 3.5: Born-Rule Multi-Shot Scale Refinement (Q2_K)
*
* 2D Born sampling: sample coarse quhit (d dimension) and
* fine quhit (dmin dimension) jointly from triality marginals.
* Each shot produces a (d_idx, dmin_idx) pair per block.
* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
{
#define Q2K_BORN_SHOTS 64
float beam_total_err = 0.0f;
for (int64_t bi = 0; bi < n_blocks; bi++)
beam_total_err += candidate_errors[bi][best_candidate[bi]];
unsigned int born_rng_q2 = 271828;
int *shot_assignment = (int *)malloc(n_blocks * sizeof(int));
for (int shot = 0; shot < Q2K_BORN_SHOTS; shot++) {
float shot_err = 0.0f;
/* Init from beam result so tail blocks beyond
* graph_blocks*stride keep valid indices */
memcpy(shot_assignment, best_candidate,
n_blocks * sizeof(int));
for (int64_t gi = 0; gi < graph_blocks; gi++) {
/* Born sample coarse (d) quhit */
double c_total = 0.0;
for (int v = 0; v < 6; v++) c_total += coarse_marg[gi][v];
born_rng_q2 = born_rng_q2 * 1664525u + 1013904223u;
double rnd_c = (double)(born_rng_q2 >> 8) / 16777216.0;
double target_c = rnd_c * c_total;
double cum_c = 0.0;
int qi_d = 5;
for (int v = 0; v < 6; v++) {
cum_c += coarse_marg[gi][v];
if (cum_c > target_c) { qi_d = v; break; }
}
/* Born sample fine (dmin) quhit */
double f_total = 0.0;
for (int v = 0; v < 6; v++) f_total += fine_marg[gi][v];
born_rng_q2 = born_rng_q2 * 1664525u + 1013904223u;
double rnd_f = (double)(born_rng_q2 >> 8) / 16777216.0;
double target_f = rnd_f * f_total;
double cum_f = 0.0;
int qi_m = 5;
for (int v = 0; v < 6; v++) {
cum_f += fine_marg[gi][v];
if (cum_f > target_f) { qi_m = v; break; }
}
/* Find best candidate within the sampled (d_bin, m_bin) */
int64_t blk = gi * stride;
float best_bin_err = 1e30f;
int best_bin_cand = 10 * N_CAND_M + 10;
for (int di = 0; di < N_CAND_D; di++) {
if (CAND_TO_QUHIT[di] != qi_d) continue;
for (int mi = 0; mi < N_CAND_M; mi++) {
if (CAND_TO_QUHIT[mi] != qi_m) continue;
int cidx = di * N_CAND_M + mi;
if (candidate_errors[blk][cidx] < best_bin_err) {
best_bin_err = candidate_errors[blk][cidx];
best_bin_cand = cidx;
}
}
}
shot_assignment[blk] = best_bin_cand;
shot_err += candidate_errors[blk][best_bin_cand];
}
if (shot_err < beam_total_err) {
for (int64_t b = 0; b < n_blocks; b++)
best_candidate[b] = shot_assignment[b];
beam_total_err = shot_err;
}
}
free(shot_assignment);
}
free(coarse_marg);
free(fine_marg);
hpc_destroy(graph);
}
} else {
/* OPT_MSE or single block: pick candidate with lowest raw error */
for (int64_t blk = 0; blk < n_blocks; blk++) {
float best_err = candidate_errors[blk][0];
int best_idx = 0;
for (int c = 1; c < TOTAL_SCALE_CANDIDATES; c++) {
if (candidate_errors[blk][c] < best_err) {
best_err = candidate_errors[blk][c];
best_idx = c;
}
}
best_candidate[blk] = best_idx;
}
}
/* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* PHASE 4: Assemble blocks via least-squares (d, dmin) extraction
*
* Like Q4_0's CF analog: the beam search / Born shots selected a
* grid candidate (d_grid, dmin_grid). Now we EXTRACT the exact
* optimal FP16 (d, dmin) via weighted least-squares, holding the
* sub-block Ls/Lm and quantized levels fixed.
*
* Q2_K model: x[j,k] β d Γ Ls[j] Γ q[j,k] - dmin Γ Lm[j]
*
* Full analog assembly: at each iteration, EXHAUSTIVELY search
* all 16Γ16 = 256 possible (Ls[j], Lm[j]) pairs per sub-block
* to find the assignment that minimizes weighted reconstruction
* error. Then WLS-solve for the global (d, dmin). Repeat 5Γ.
*
* This guarantees every parameter is at its conditional optimum β
* the perfect bit analog at 2-bit resolution.
* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
#pragma omp parallel for schedule(dynamic, 64) reduction(+:total_err)
for (int64_t blk = 0; blk < n_blocks; blk++) {
const float *block_x = weights + blk * QK_K;
int cidx = best_candidate[blk];
uint8_t Ls_blk[16], Lm_blk[16];
/* Start from HPC-selected candidate */
memcpy(Ls_blk, candidate_Ls[blk][cidx], 16);
memcpy(Lm_blk, candidate_Lm[blk][cidx], 16);
float dm = gguf_fp16_to_fp32(candidate_d[blk][cidx]);
float mm = gguf_fp16_to_fp32(candidate_dmin[blk][cidx]);
/* ββ Analog assembly: iterate to convergence ββ
* 5 iterations: enough for the (Ls,Lm) β (d,dmin) coupling
* to fully stabilize. Each iteration does:
* A) Sub-block Quhit BP to find coupled (Ls,Lm) states
* B) Optimal q-value assignment
* C) WLS solve for (d, dmin) */
for (int ls_iter = 0; ls_iter < 8; ls_iter++) {
/* ββ Step A: Sub-block Quhit BP (Strategy 1) ββ
* For each sub-block j, evaluate all 256 (Ls, Lm) pairs.
* Keep the 6 best pairs as quhit states for a 16-node graph.
* Run BP to jointly select the globally optimal (Ls, Lm). */
uint8_t state_ls[N_SUB][6];
uint8_t state_lm[N_SUB][6];
float state_err[N_SUB][6];
for (int j = 0; j < N_SUB; j++) {
const float *sx = block_x + 16 * j;
for (int v = 0; v < 6; v++) state_err[j][v] = 1e30f;
for (int try_ls = 0; try_ls <= 15; try_ls++) {
float d_sub = dm * (float)try_ls;
for (int try_lm = 0; try_lm <= 15; try_lm++) {
float m_sub = mm * (float)try_lm;
float sub_err = 0.0f;
for (int k = 0; k < 16; k++) {
float x = sx[k];
float w = (imat_importance) ?
imat_importance[blk * QK_K + 16*j + k] : 1.0f;
int q = 0;
if (d_sub >= 1e-15f) {
q = gguf_nearest_int((x + m_sub) / d_sub);
if (q < 0) q = 0; if (q > 3) q = 3;
}
float deq = d_sub * (float)q - m_sub;
float diff = x - deq;
sub_err += diff * diff * w;
}
/* Insert into top 6 */
for (int v = 0; v < 6; v++) {
if (sub_err < state_err[j][v]) {
for (int u = 5; u > v; u--) {
state_err[j][u] = state_err[j][u-1];
state_ls[j][u] = state_ls[j][u-1];
state_lm[j][u] = state_lm[j][u-1];
}
state_err[j][v] = sub_err;
state_ls[j][v] = (uint8_t)try_ls;
state_lm[j][v] = (uint8_t)try_lm;
break;
}
}
}
}
}
/* Build 16-node sub-block graph and run BP */
HPCGraph *sg = hpc_create(N_SUB);
if (sg) {
float min_sub_err[N_SUB];
for (int j = 0; j < N_SUB; j++) min_sub_err[j] = state_err[j][0];
/* Initialize unary potentials from local errors */
for (int j = 0; j < N_SUB; j++) {
triality_dft(&sg->locals[j]);
double amp_re[6];
double amp_norm = 0.0;
for (int v = 0; v < 6; v++) {
/* Adaptive temperature: scale with local error spread
* so Shor measurement produces meaningful interference
* patterns regardless of weight magnitude */
float err_spread = state_err[j][5] - state_err[j][0];
float sub_temp = (err_spread > 1e-15f) ? err_spread * 0.3f : 0.1f;
if (sub_temp < 1e-12f) sub_temp = 1e-12f;
amp_re[v] = exp(-(double)(state_err[j][v] - min_sub_err[j]) / (double)sub_temp);
amp_norm += amp_re[v] * amp_re[v];
}
if (amp_norm > 1e-30) {
double inv = 1.0 / sqrt(amp_norm);
for (int v = 0; v < 6; v++) amp_re[v] *= inv;
}
for (int v = 0; v < 6; v++) {
sg->locals[j].edge_re[v] = amp_re[v];
sg->locals[j].edge_im[v] = 0.0;
}
sg->locals[j].primary = VIEW_EDGE;
sg->locals[j].dirty = DIRTY_VERTEX | DIRTY_DIAGONAL | DIRTY_FOLDED;
sg->locals[j].delta_valid = 0;
triality_update_mask(&sg->locals[j]);
}
/* Add coupling edges between adjacent sub-blocks */
for (int j = 0; j < N_SUB - 1; j++)
hpc_cz(sg, j, j + 1);
/* ββ Shor sequential measurement on sub-block graph ββ
* Replaces BP with exact marginals (ported from tesseract_factor.c) */
double (*sub_marg)[6] = (double (*)[6])calloc(N_SUB, sizeof(double[6]));
int *sub_measured = (int *)calloc(N_SUB, sizeof(int));
shor_measure_graph(sg, N_SUB, sub_marg, sub_measured, 1);
/* Extract optimal Ls/Lm from Shor marginals */
for (int j = 0; j < N_SUB; j++) {
double best_prob = -1.0;
int best_v = 0;
for (int v = 0; v < 6; v++) {
if (sub_marg[j][v] > best_prob) {
best_prob = sub_marg[j][v];
best_v = v;
}
}
Ls_blk[j] = state_ls[j][best_v];
Lm_blk[j] = state_lm[j][best_v];
}
free(sub_marg);
free(sub_measured);
hpc_destroy(sg);
} else {
/* Fallback to independent local optima if malloc fails */
for (int j = 0; j < N_SUB; j++) {
Ls_blk[j] = state_ls[j][0];
Lm_blk[j] = state_lm[j][0];
}
}
/* ββ Step B: Quantize q-values with optimal Ls/Lm ββ */
uint8_t L[QK_K];
for (int j = 0; j < N_SUB; j++) {
float d_sub = dm * (float)Ls_blk[j];
float m_sub = mm * (float)Lm_blk[j];
if (d_sub < 1e-15f) {
for (int k = 0; k < 16; k++) L[16*j+k] = 0;
continue;
}
for (int k = 0; k < 16; k++) {
int q = gguf_nearest_int((block_x[16*j+k] + m_sub) / d_sub);
if (q < 0) q = 0; if (q > 3) q = 3;
L[16*j+k] = (uint8_t)q;
}
}
/* ββ Step C: WLS solve for (d, dmin) ββ
* x[j,k] β d Γ Ls[j] Γ q[j,k] - dmin Γ Lm[j]
* Let a = Ls[j]Γq[j,k], b = Lm[j]
* Normal equations via Cramer's rule */
double Saa = 0, Sab = 0, Sbb = 0, Sxa = 0, Sxb = 0;
for (int j = 0; j < N_SUB; j++) {
float ls_f = (float)Ls_blk[j];
float lm_f = (float)Lm_blk[j];
for (int k = 0; k < 16; k++) {
float x = block_x[16*j+k];
float w = (imat_importance) ?
imat_importance[blk * QK_K + 16*j+k] : 1.0f;
float a = ls_f * (float)L[16*j+k];
float b = lm_f;
Saa += w * a * a;
Sab += w * a * b;
Sbb += w * b * b;
Sxa += w * x * a;
Sxb += w * x * b;
}
}
double det = Saa * Sbb - Sab * Sab;
if (fabs(det) > 1e-30) {
double d_new = (Sbb * Sxa - Sab * Sxb) / det;
double dm_new = (Sab * Sxa - Saa * Sxb) / det;
/* Clamp: positive and within 4Γ of candidate seed */
float d_seed = gguf_fp16_to_fp32(candidate_d[blk][cidx]);
float m_seed = gguf_fp16_to_fp32(candidate_dmin[blk][cidx]);
if (d_new > 0.0 && d_new < 4.0 * (d_seed + 1e-10))
dm = gguf_fp16_to_fp32(gguf_fp32_to_fp16((float)d_new));
if (dm_new > 0.0 && dm_new < 4.0 * (m_seed + 1e-10))
mm = gguf_fp16_to_fp32(gguf_fp32_to_fp16((float)dm_new));
}
if (isnan(dm) || isnan(mm)) {
printf("NaN detected before ULP: dm=%f mm=%f det=%f\n", dm, mm, det);
exit(1);
}
}
/* ββ FP16 ULP neighborhood search for (d, dmin) ββ
* The WLS solve found continuous-optimal (d, dmin). But FP16
* truncation may shift the optimum. Try Β±4 ULP around both
* d and dmin, pick the pair with minimum reconstruction error. */
{
uint16_t base_d16 = gguf_fp32_to_fp16(dm);
uint16_t base_m16 = gguf_fp32_to_fp16(mm);
uint16_t best_d16 = base_d16, best_m16 = base_m16;
float best_ulp_err = 1e30f;
for (int dd = -2; dd <= 2; dd++) {
int cd16 = (int)base_d16 + dd;
if (cd16 < 0 || cd16 > 0x7BFF) continue;
float trial_dm = gguf_fp16_to_fp32((uint16_t)cd16);
for (int dm_delta = -2; dm_delta <= 2; dm_delta++) {
int cm16 = (int)base_m16 + dm_delta;
if (cm16 < 0 || cm16 > 0x7BFF) continue;
float trial_mm = gguf_fp16_to_fp32((uint16_t)cm16);
float err = 0.0f;
for (int j = 0; j < N_SUB; j++) {
float d_sub = trial_dm * (float)Ls_blk[j];
float m_sub = trial_mm * (float)Lm_blk[j];
for (int k = 0; k < 16; k++) {
float x = block_x[16*j+k];
float w = (imat_importance) ?
imat_importance[blk * QK_K + 16*j+k] : 1.0f;
int q;
if (d_sub < 1e-15f) { q = 0; }
else {
q = gguf_nearest_int((x + m_sub) / d_sub);
if (q < 0) q = 0; if (q > 3) q = 3;
}
float deq = d_sub * (float)q - m_sub;
float diff = x - deq;
err += diff * diff * w;
}
}
if (err < best_ulp_err) {
best_ulp_err = err;
best_d16 = (uint16_t)cd16;
best_m16 = (uint16_t)cm16;
}
}
}
dm = gguf_fp16_to_fp32(best_d16);
mm = gguf_fp16_to_fp32(best_m16);
}
/* ββ Final Ls/Lm re-optimization at committed FP16 (d, dmin) ββ
* The WLS solve may have shifted (d, dmin) after the last Step A,
* invalidating the Ls/Lm choices. One final exhaustive pass at the
* EXACT FP16-truncated scales ensures every sub-block is optimal. */
for (int j = 0; j < N_SUB; j++) {
const float *sx = block_x + 16 * j;
float best_sub_err = 1e30f;
uint8_t best_ls = Ls_blk[j], best_lm = Lm_blk[j];
for (int try_ls = 0; try_ls <= 15; try_ls++) {
float d_sub = dm * (float)try_ls;
for (int try_lm = 0; try_lm <= 15; try_lm++) {
float m_sub = mm * (float)try_lm;
float sub_err = 0.0f;
for (int k = 0; k < 16; k++) {
float x = sx[k];
float w = (imat_importance) ?
imat_importance[blk * QK_K + 16*j + k] : 1.0f;
int q;
if (d_sub < 1e-15f) { q = 0; }
else {
q = gguf_nearest_int((x + m_sub) / d_sub);
if (q < 0) q = 0; if (q > 3) q = 3;
}
float deq = d_sub * (float)q - m_sub;
float diff = x - deq;
sub_err += diff * diff * w;
}
if (sub_err < best_sub_err) {
best_sub_err = sub_err;
best_ls = (uint8_t)try_ls;
best_lm = (uint8_t)try_lm;
}
}
}
Ls_blk[j] = best_ls;
Lm_blk[j] = best_lm;
}
/* Store the extracted optimal FP16 (d, dmin) */
output[blk].d = gguf_fp32_to_fp16(dm);
output[blk].dmin = gguf_fp32_to_fp16(mm);
for (int j = 0; j < N_SUB; j++)
output[blk].scales[j] = Ls_blk[j] | (Lm_blk[j] << 4);
/* ββ Final quantization with Dβ Hadamard Error Shaping ββ
*
* Standard Q2_K rounds each weight independently: q = round((x+m)/d).
* But within a sub-block, weights share (d, m), so their quantization
* errors are CORRELATED. Independent rounding is suboptimal.
*
* The Dβ fold (antipodal Hadamard from the triality quhit) decomposes
* the error vector into vesica (sum) and wave (difference) components:
* vesica[k] = (e[k] + e[k+3]) / β2 β DC-like, accumulates in dot products
* wave[k] = (e[k] - e[k+3]) / β2 β noise-like, cancels in dot products
*
* We WANT large wave error and small vesica error. So we greedily
* flip rounding decisions (floorβceil) to minimize vesica energy,
* even if total element-wise error increases slightly.
*
* Process: 16 elements per sub-block, treat as 2 groups of 6 + 4 tail.
* Apply DFTβ-fold to each group of 6, minimize vesica component.
*/
uint8_t L[QK_K];
for (int j = 0; j < N_SUB; j++) {
float d = dm * (float)(output[blk].scales[j] & 0xF);
if (d < 1e-15f) {
for (int k = 0; k < 16; k++) L[16 * j + k] = 0;
continue;
}
float m = mm * (float)(output[blk].scales[j] >> 4);
float id = 1.0f / d;
/* Step 1: Standard nearest-rounding as baseline */
int q_base[16];
float q_cont[16]; /* continuous q values before rounding */
for (int k = 0; k < 16; k++) {
q_cont[k] = (block_x[16*j+k] + m) * id;
q_base[k] = gguf_nearest_int(q_cont[k]);
if (q_base[k] < 0) q_base[k] = 0;
if (q_base[k] > 3) q_base[k] = 3;
}
/* Step 2: Dβ Hadamard Error Shaping
* For each 6-element group, greedily flip the rounding decision
* that most reduces the Dβ-folded vesica error component.
*
* Dβ fold on 6-element groups: antipodal pairs (0,3), (1,4), (2,5)
* vesica[k] = e[k] + e[k+3] (k=0,1,2) β DC-like, propagates
* wave[k] = e[k] - e[k+3] (k=0,1,2) β noise-like, cancels
*
* Weight vesica 4Γ over wave + penalize DC (sum of all 6 errors) */
int q_shaped[16];
memcpy(q_shaped, q_base, 16 * sizeof(int));
/* Process groups: [0..5], [6..11], tail [12..15] handled by Dβ metric on available pairs */
for (int g = 0; g < 2; g++) {
int g_off = g * 6;
if (g_off + 5 >= 16) break;
/* Multiple greedy passes β each pass finds the single best flip */
for (int pass = 0; pass < 6; pass++) {
int best_k = -1;
int best_q_alt = 0;
float best_delta = 0.0f; /* improvement = current_metric - alt_metric */
/* Compute current group errors */
float e_cur[6];
for (int kk = 0; kk < 6; kk++) {
int ii = g_off + kk;
float deq = d * (float)q_shaped[ii] - m;
e_cur[kk] = block_x[16*j+ii] - deq;
}
/* Current Dβ metric: vesica energy + DCΒ² */
float vesica_cur = 0.0f, dc_cur = 0.0f;
for (int p = 0; p < 3; p++) {
float v = e_cur[p] + e_cur[p+3];
vesica_cur += v * v;
}
for (int kk = 0; kk < 6; kk++) dc_cur += e_cur[kk];
float metric_cur = 4.0f * vesica_cur + dc_cur * dc_cur;
/* Try flipping each element */
for (int k = 0; k < 6; k++) {
int idx = g_off + k;
int q_cur = q_shaped[idx];
/* Try the alternative rounding */
int q_try;
if (q_cont[idx] - (float)q_cur >= 0) {
q_try = q_cur + 1;
} else {
q_try = q_cur - 1;
}
if (q_try < 0 || q_try > 3) continue;
/* Compute alt errors (only element k changes) */
float e_alt[6];
for (int kk = 0; kk < 6; kk++) e_alt[kk] = e_cur[kk];
float deq_try = d * (float)q_try - m;
e_alt[k] = block_x[16*j+idx] - deq_try;
/* Alt Dβ metric */
float vesica_alt = 0.0f, dc_alt = 0.0f;
for (int p = 0; p < 3; p++) {
float v = e_alt[p] + e_alt[p+3];
vesica_alt += v * v;
}
for (int kk = 0; kk < 6; kk++) dc_alt += e_alt[kk];
float metric_alt = 4.0f * vesica_alt + dc_alt * dc_alt;
float delta = metric_cur - metric_alt;
if (delta > best_delta) {
best_delta = delta;
best_k = k;
best_q_alt = q_try;
}
}
if (best_k < 0) break; /* no improvement found */
q_shaped[g_off + best_k] = best_q_alt; /* commit the flip */
}
}
/* Step 3: Final error comparison β only keep shaped if it improves
* or is within 5% of baseline (vesica shaping trades element MSE
* for better spectral distribution of error) */
float err_base = 0.0f, err_shaped = 0.0f;
for (int k = 0; k < 16; k++) {
float x = block_x[16*j+k];
float w = (imat_importance) ?
imat_importance[blk * QK_K + 16*j + k] : 1.0f;
float deq_b = d * (float)q_base[k] - m;
float deq_s = d * (float)q_shaped[k] - m;
err_base += (x - deq_b) * (x - deq_b) * w;
err_shaped += (x - deq_s) * (x - deq_s) * w;
}
int *q_final = (err_shaped <= err_base * 1.05f) ? q_shaped : q_base;
for (int k = 0; k < 16; k++)
L[16 * j + k] = (uint8_t)q_final[k];
}
for (int j = 0; j < QK_K; j += 128) {
for (int l = 0; l < 32; l++) {
output[blk].qs[j / 4 + l] = L[j + l]
| (L[j + l + 32] << 2)
| (L[j + l + 64] << 4)
| (L[j + l + 96] << 6);
}
}
float berr = gguf_q2_k_block_error(block_x, &output[blk]);
if (isnan(berr)) {
printf("NaN block error at blk %ld! dm=%f mm=%f\n", (long)blk, dm, mm);
for (int j=0; j<16; j++) printf("Ls[%d]=%d Lm[%d]=%d\n", j, Ls_blk[j], j, Lm_blk[j]);
exit(1);
}
total_err += berr;
}
free(seeds);
free(candidate_errors);
free(candidate_d);
free(candidate_dmin);
free(candidate_Ls);
free(candidate_Lm);
free(best_candidate);
if (out_total_error) *out_total_error = total_err;
if (verbose) {
float rmse = sqrtf(total_err / (float)n_elements);
/* Compute weight Ο for fidelity classification */
double w_sum2 = 0.0;
for (int64_t i = 0; i < n_elements; i++)
w_sum2 += (double)weights[i] * (double)weights[i];
float w_sigma = (float)sqrt(w_sum2 / (double)n_elements);
float rmse_over_sigma = (w_sigma > 1e-15f) ? rmse / w_sigma : 0.0f;
/* Fidelity classification */
const char *fidelity_class;
const char *fidelity_icon;
if (rmse <= 1.0e-04f) {
fidelity_class = "ULTRA (β€1e-04)";
fidelity_icon = "β
β
β
β
";
} else if (rmse <= 3.0e-04f) {
fidelity_class = "HIGH (β€3e-04)";
fidelity_icon = "β
β
β
β";
} else if (rmse <= 1.0e-03f) {
fidelity_class = "GOOD (β€1e-03)";
fidelity_icon = "β
β
ββ";
} else {
fidelity_class = "STANDARD";
fidelity_icon = "β
βββ";
}
printf("\n βββββ Shor Measurement Q2_K Report βββββββββββββββββββββββββββββββββ\n");
printf(" β Elements: %-12lld Blocks: %-12lld β\n",
(long long)n_elements, (long long)(n_elements / QK_K));
printf(" β Weight Ο: %-12.4e Range: [%.4e, %.4e] β\n",
w_sigma, w_sigma * -4.0f, w_sigma * 4.0f);
printf(" β Total MSE: %-12.6f β\n", total_err);
printf(" β RMSE: %-12.4e RMSE/Ο: %-8.4f β\n",
rmse, rmse_over_sigma);
printf(" β Fidelity: %s %-14s β\n",
fidelity_icon, fidelity_class);
printf(" β Engine: Shor Griffiths-Niu (IDFT6 + feed-forward) β\n");
printf(" βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ\n");
}
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* PROGRESS REPORTING
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
static void print_progress_bar(int current, int total, const char *label,
clock_t start_time)
{
if (total <= 0) return;
float pct = (float)current / (float)total;
int bar_width = 40;
int filled = (int)(pct * bar_width);
double elapsed = (double)(clock() - start_time) / CLOCKS_PER_SEC;
double eta = (pct > 0.01f) ? elapsed / pct * (1.0 - pct) : 0.0;
printf("\r [");
for (int i = 0; i < bar_width; i++) {
if (i < filled) printf("β");
else if (i == filled) printf("β");
else printf("β");
}
printf("] %3d%% (%d/%d) %.0fs ETA:%.0fs %s",
(int)(pct * 100), current, total, elapsed, eta, label);
fflush(stdout);
if (current == total) printf("\n");
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* GGUF FILE WRITER β Assembles the complete output file
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
static int write_gguf(const char *output_path, const STMultiFile *mf,
const ModelArchitecture *arch,
const TokenizerData *tokenizer,
OptimizerMode opt_mode,
const IMatrixData *imatrix,
int verbose)
{
FILE *fp = fopen(output_path, "wb");
if (!fp) {
fprintf(stderr, " ERROR: Cannot open '%s' for writing\n", output_path);
return -1;
}
printf("\n ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ\n");
printf(" β WRITING GGUF FILE β\n");
printf(" ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ\n\n");
/* ββ Determine which tensors to include ββ */
int *include_list = (int *)calloc(mf->n_tensors, sizeof(int));
int n_include = 0;
for (int i = 0; i < mf->n_tensors; i++) {
if (!should_skip_tensor(mf->tensor_map[i].name)) {
include_list[n_include++] = i;
} else {
if (verbose) printf(" SKIP: %s (not needed in GGUF)\n", mf->tensor_map[i].name);
}
}
/* ββ Count metadata KV pairs ββ */
int n_kv = 0;
n_kv++; /* general.architecture */
n_kv++; /* general.name */
n_kv++; /* general.quantization_version */
n_kv++; /* general.file_type */
n_kv++; /* {arch}.context_length */
n_kv++; /* {arch}.embedding_length */
n_kv++; /* {arch}.block_count */
n_kv++; /* {arch}.feed_forward_length */
n_kv++; /* {arch}.attention.head_count */
n_kv++; /* {arch}.attention.head_count_kv */
n_kv++; /* {arch}.attention.layer_norm_rms_epsilon */
n_kv++; /* {arch}.rope.freq_base */
n_kv++; /* {arch}.vocab_size */
/* Tokenizer metadata KV count */
int has_tokenizer = (tokenizer != NULL && tokenizer->vocab_size > 0);
if (has_tokenizer) {
n_kv++; /* tokenizer.ggml.model */
n_kv++; /* tokenizer.ggml.tokens */
n_kv++; /* tokenizer.ggml.scores */
n_kv++; /* tokenizer.ggml.token_type */
n_kv++; /* tokenizer.ggml.bos_token_id */
n_kv++; /* tokenizer.ggml.eos_token_id */
n_kv++; /* tokenizer.ggml.unknown_token_id */
if (tokenizer->n_merges > 0)
n_kv++; /* tokenizer.ggml.merges */
}
/* ββ Check for weight tying ββ
* If tie_word_embeddings is set and there's no separate lm_head,
* llama.cpp handles this internally β do NOT duplicate the tensor.
* Only add output.weight if the model has a separate lm_head.weight. */
int has_lm_head = (st_multi_find_tensor(mf, "lm_head.weight") >= 0);
int total_tensors = n_include;
if (arch->tie_word_embeddings && !has_lm_head) {
printf(" Weight-tied embeddings detected β llama.cpp handles internally\n\n");
}
/* ββ Prepare tensor info ββ */
char (*gguf_names)[ST_MAX_NAME_LEN] = calloc(total_tensors, ST_MAX_NAME_LEN);
GGMLType *tensor_types = calloc(total_tensors, sizeof(GGMLType));
int64_t *tensor_sizes = calloc(total_tensors, sizeof(int64_t));
uint64_t data_offset = 0;
uint64_t *tensor_offsets = calloc(total_tensors, sizeof(uint64_t));
int *tensor_src_idx = calloc(total_tensors, sizeof(int)); /* map to unified ST index */
char (*tensor_hf_names)[ST_MAX_NAME_LEN] = calloc(total_tensors, ST_MAX_NAME_LEN);
GGMLType quant_type = GGML_TYPE_Q2_K;
for (int i = 0; i < n_include; i++) {
int src = include_list[i];
const STTensorInfo *ti = st_multi_tensor_info(mf, src);
map_tensor_name(mf->tensor_map[src].name, gguf_names[i], ST_MAX_NAME_LEN);
strncpy(tensor_hf_names[i], mf->tensor_map[src].name, ST_MAX_NAME_LEN - 1);
tensor_src_idx[i] = src;
if (should_quantize(ti, gguf_names[i])) {
if (is_attention_tensor(gguf_names[i])) {
/* Promote attention Q/K/V/O to Q4_0 for higher precision.
* Attention scores are most sensitive to quantization noise. */
tensor_types[i] = GGML_TYPE_Q4_0;
int64_t n_blocks_q4 = (ti->n_elements + QK4_0 - 1) / QK4_0;
tensor_sizes[i] = n_blocks_q4 * sizeof(BlockQ4_0);
if (verbose)
printf(" [ATTNβQ4_0] %s (%ld elements)\n",
gguf_names[i], (long)ti->n_elements);
} else {
tensor_types[i] = quant_type;
tensor_sizes[i] = ggml_type_size(quant_type, ti->n_elements);
}
} else if (ti->n_dims >= 2) {
/* 2D non-quantized tensors (embeddings, output) β F16 */
tensor_types[i] = GGML_TYPE_F16;
tensor_sizes[i] = ti->n_elements * sizeof(uint16_t);
} else {
/* 1D tensors (norms, biases) β F32 */
tensor_types[i] = GGML_TYPE_F32;
tensor_sizes[i] = ti->n_elements * sizeof(float);
}
tensor_offsets[i] = data_offset;
/* Align each tensor to 32 bytes */
data_offset += tensor_sizes[i];
data_offset = (data_offset + GGUF_DEFAULT_ALIGNMENT - 1) &
~(uint64_t)(GGUF_DEFAULT_ALIGNMENT - 1);
}
/* ββ Write header ββ */
gguf_write_header(fp, total_tensors, n_kv);
/* ββ Write metadata KV pairs ββ */
gguf_write_kv_string(fp, "general.architecture", arch->architecture);
gguf_write_kv_string(fp, "general.name", arch->name);
gguf_write_kv_uint32(fp, "general.quantization_version", 2);
gguf_write_kv_uint32(fp, "general.file_type", 10); /* Q2_K = 10 */
char kbuf[128];
snprintf(kbuf, sizeof(kbuf), "%s.context_length", arch->architecture);
gguf_write_kv_uint32(fp, kbuf, arch->context_length);
snprintf(kbuf, sizeof(kbuf), "%s.embedding_length", arch->architecture);
gguf_write_kv_uint32(fp, kbuf, arch->embedding_length);
snprintf(kbuf, sizeof(kbuf), "%s.block_count", arch->architecture);
gguf_write_kv_uint32(fp, kbuf, arch->block_count);
snprintf(kbuf, sizeof(kbuf), "%s.feed_forward_length", arch->architecture);
gguf_write_kv_uint32(fp, kbuf, arch->feed_forward_length);
snprintf(kbuf, sizeof(kbuf), "%s.attention.head_count", arch->architecture);
gguf_write_kv_uint32(fp, kbuf, arch->head_count);
snprintf(kbuf, sizeof(kbuf), "%s.attention.head_count_kv", arch->architecture);
gguf_write_kv_uint32(fp, kbuf, arch->head_count_kv);
snprintf(kbuf, sizeof(kbuf), "%s.attention.layer_norm_rms_epsilon", arch->architecture);
gguf_write_kv_float32(fp, kbuf, arch->rms_norm_eps);
snprintf(kbuf, sizeof(kbuf), "%s.rope.freq_base", arch->architecture);
gguf_write_kv_float32(fp, kbuf, arch->rope_freq_base);
snprintf(kbuf, sizeof(kbuf), "%s.vocab_size", arch->architecture);
gguf_write_kv_uint32(fp, kbuf, arch->vocab_size);
/* ββ Write tokenizer metadata ββ */
if (has_tokenizer) {
gguf_write_kv_string(fp, "tokenizer.ggml.model", tokenizer->model_type);
gguf_write_kv_string_array(fp, "tokenizer.ggml.tokens",
(const char **)tokenizer->tokens,
(uint64_t)tokenizer->vocab_size);
gguf_write_kv_float32_array(fp, "tokenizer.ggml.scores",
tokenizer->scores,
(uint64_t)tokenizer->vocab_size);
gguf_write_kv_int32_array(fp, "tokenizer.ggml.token_type",
tokenizer->token_types,
(uint64_t)tokenizer->vocab_size);
gguf_write_kv_uint32(fp, "tokenizer.ggml.bos_token_id",
(uint32_t)tokenizer->bos_id);
gguf_write_kv_uint32(fp, "tokenizer.ggml.eos_token_id",
(uint32_t)tokenizer->eos_id);
gguf_write_kv_uint32(fp, "tokenizer.ggml.unknown_token_id",
(uint32_t)tokenizer->unk_id);
if (tokenizer->n_merges > 0) {
gguf_write_kv_string_array(fp, "tokenizer.ggml.merges",
(const char **)tokenizer->merges,
(uint64_t)tokenizer->n_merges);
}
printf(" Tokenizer metadata written (%d tokens, %d merges)\n\n",
tokenizer->vocab_size, tokenizer->n_merges);
}
/* ββ Write tensor info descriptors ββ */
for (int i = 0; i < total_tensors; i++) {
int src = tensor_src_idx[i];
const STTensorInfo *ti = st_multi_tensor_info(mf, src);
uint64_t dims[ST_MAX_DIMS];
/* GGUF uses reversed dimension order from SafeTensors/PyTorch */
int nd = ti->n_dims;
for (int d = 0; d < nd; d++) {
dims[d] = (uint64_t)ti->shape[nd - 1 - d];
}
gguf_write_tensor_info(fp, gguf_names[i],
ti->n_dims, dims,
tensor_types[i], tensor_offsets[i]);
}
/* ββ Alignment padding before data section ββ */
gguf_write_padding(fp, GGUF_DEFAULT_ALIGNMENT);
/* ββ Write tensor data ββ */
printf(" Quantizing and writing %d tensors...\n\n", total_tensors);
float total_error_sum = 0.0f;
int quant_count = 0;
int64_t total_elements_quantized = 0;
int64_t total_bytes_quantized = 0;
int64_t total_bytes_unquantized = 0;
clock_t quant_start = clock();
for (int i = 0; i < total_tensors; i++) {
int src = tensor_src_idx[i];
const STTensorInfo *ti = st_multi_tensor_info(mf, src);
print_progress_bar(i, total_tensors, gguf_names[i], quant_start);
if (tensor_types[i] == GGML_TYPE_Q2_K) {
/* ββ HPC-Optimized Q2_K Quantization ββ */
float *f32_data = st_multi_tensor_to_f32(mf, src);
if (!f32_data) {
fprintf(stderr, "\n ERROR: Failed to convert tensor '%s' to F32\n",
ti->name);
continue;
}
int64_t n_elements = ti->n_elements;
float tensor_error = 0.0f;
/* Pad to QK_K boundary */
int64_t padded = (n_elements + QK_K - 1) / QK_K * QK_K;
if (padded > n_elements) {
f32_data = realloc(f32_data, padded * sizeof(float));
for (int64_t j = n_elements; j < padded; j++)
f32_data[j] = 0.0f;
n_elements = padded;
}
int64_t n_blocks = n_elements / QK_K;
BlockQ2K *quant_data = calloc(n_blocks, sizeof(BlockQ2K));
/* Look up imatrix importance for this tensor */
const float *imp = NULL;
if (imatrix) {
const IMatrixEntry *ime = imatrix_find_any(imatrix,
gguf_names[i], tensor_hf_names[i]);
if (ime && ime->n_values > 0) {
imp = ime->normalized;
if (verbose)
printf("\n imatrix: using %d importance weights for %s\n",
ime->n_values, gguf_names[i]);
}
}
quantize_tensor_q2k_hpc(f32_data, n_elements,
quant_data, &tensor_error,
opt_mode, imp, verbose);
fwrite(quant_data, sizeof(BlockQ2K), n_blocks, fp);
float rmse = sqrtf(tensor_error / (float)ti->n_elements);
/* Compute weight Ο for fidelity gate */
double wss = 0.0;
for (int64_t j = 0; j < ti->n_elements; j++)
wss += (double)f32_data[j] * (double)f32_data[j];
float w_sig = (float)sqrt(wss / (double)ti->n_elements);
/* Fidelity gate: classify RMSE vs 1e-04 target */
const char *fid;
if (rmse <= 1.0e-04f) fid = "β
β
β
β
ULTRA";
else if (rmse <= 3.0e-04f) fid = "β
β
β
β HIGH";
else if (rmse <= 1.0e-03f) fid = "β
β
ββ GOOD";
else fid = "β
βββ STD";
if (verbose) {
printf("\n [Q2_KΒ·Shor] %-47s\n", gguf_names[i]);
printf(" %10ld elements β %ld bytes Ο=%.2e RMSE=%.4e %s\n",
(long)ti->n_elements,
(long)(n_blocks * sizeof(BlockQ2K)),
w_sig, rmse, fid);
}
total_error_sum += tensor_error;
total_elements_quantized += ti->n_elements;
total_bytes_quantized += n_blocks * sizeof(BlockQ2K);
quant_count++;
free(quant_data);
free(f32_data);
} else if (tensor_types[i] == GGML_TYPE_Q4_0) {
/* ββ HPC-Optimized Q4_0 Quantization (attention tensors) ββ */
float *f32_data = st_multi_tensor_to_f32(mf, src);
if (!f32_data) {
fprintf(stderr, "\n ERROR: Failed to convert tensor '%s' to F32\n",
ti->name);
continue;
}
int64_t n_elements = ti->n_elements;
/* Pad to QK4_0 boundary */
int64_t padded = (n_elements + QK4_0 - 1) / QK4_0 * QK4_0;
if (padded > n_elements) {
f32_data = realloc(f32_data, padded * sizeof(float));
for (int64_t j = n_elements; j < padded; j++)
f32_data[j] = 0.0f;
n_elements = padded;
}
int64_t n_blocks_q4 = n_elements / QK4_0;
BlockQ4_0 *q4_data = calloc(n_blocks_q4, sizeof(BlockQ4_0));
float tensor_error = 0.0f;
/* Look up imatrix importance for this tensor */
const float *imp = NULL;
if (imatrix) {
const IMatrixEntry *ime = imatrix_find_any(imatrix,
gguf_names[i], tensor_hf_names[i]);
if (ime && ime->n_values > 0) {
imp = ime->normalized;
if (verbose)
printf("\n imatrix: using %d importance weights for %s\n",
ime->n_values, gguf_names[i]);
}
}
quantize_tensor_q4_0_hpc(f32_data, n_elements,
q4_data, &tensor_error,
imp, verbose);
fwrite(q4_data, sizeof(BlockQ4_0), n_blocks_q4, fp);
float rmse = sqrtf(tensor_error / (float)ti->n_elements);
/* Compute weight Ο for fidelity gate */
double wss4 = 0.0;
for (int64_t j = 0; j < ti->n_elements; j++)
wss4 += (double)f32_data[j] * (double)f32_data[j];
float w_sig4 = (float)sqrt(wss4 / (double)ti->n_elements);
const char *fid4;
if (rmse <= 1.0e-04f) fid4 = "β
β
β
β
ULTRA";
else if (rmse <= 3.0e-04f) fid4 = "β
β
β
β HIGH";
else if (rmse <= 1.0e-03f) fid4 = "β
β
ββ GOOD";
else fid4 = "β
βββ STD";
if (verbose) {
printf("\n [Q4_0Β·Shor] %-47s\n", gguf_names[i]);
printf(" %10ld elements β %ld bytes Ο=%.2e RMSE=%.4e %s\n",
(long)ti->n_elements,
(long)(n_blocks_q4 * sizeof(BlockQ4_0)),
w_sig4, rmse, fid4);
}
total_error_sum += tensor_error;
total_elements_quantized += ti->n_elements;
total_bytes_quantized += n_blocks_q4 * sizeof(BlockQ4_0);
quant_count++;
free(q4_data);
free(f32_data);
} else if (tensor_types[i] == GGML_TYPE_F16) {
/* ββ Store as F16 (embeddings, output, 2D non-quantized) ββ */
float *f32_data = st_multi_tensor_to_f32(mf, src);
if (!f32_data) {
fprintf(stderr, "\n ERROR: Failed to convert tensor '%s'\n",
ti->name);
continue;
}
/* Convert F32 β F16 */
uint16_t *f16_data = (uint16_t *)malloc(ti->n_elements * sizeof(uint16_t));
for (int64_t j = 0; j < ti->n_elements; j++)
f16_data[j] = gguf_fp32_to_fp16(f32_data[j]);
fwrite(f16_data, sizeof(uint16_t), ti->n_elements, fp);
total_bytes_unquantized += ti->n_elements * sizeof(uint16_t);
if (verbose) {
printf("\n [F16 ] %-50s %10ld elements β %ld bytes\n",
gguf_names[i], (long)ti->n_elements,
(long)(ti->n_elements * sizeof(uint16_t)));
}
free(f16_data);
free(f32_data);
} else {
/* ββ Keep as F32 (1D: norms, biases) ββ */
float *f32_data = st_multi_tensor_to_f32(mf, src);
if (!f32_data) {
fprintf(stderr, "\n ERROR: Failed to convert tensor '%s'\n",
ti->name);
continue;
}
fwrite(f32_data, sizeof(float), ti->n_elements, fp);
total_bytes_unquantized += ti->n_elements * sizeof(float);
if (verbose) {
printf("\n [F32 ] %-50s %10ld elements β %ld bytes\n",
gguf_names[i], (long)ti->n_elements,
(long)(ti->n_elements * sizeof(float)));
}
free(f32_data);
}
/* Pad to alignment */
gguf_write_padding(fp, GGUF_DEFAULT_ALIGNMENT);
}
print_progress_bar(total_tensors, total_tensors, "done", quant_start);
long final_size = ftell(fp);
fclose(fp);
/* ββ Final summary with Shor fidelity metrics ββ */
/* Compute original model size (all as F32) */
int64_t original_f32_size = 0;
for (int i = 0; i < total_tensors; i++) {
const STTensorInfo *ti = st_multi_tensor_info(mf, tensor_src_idx[i]);
original_f32_size += ti->n_elements * sizeof(float);
}
float compression_ratio = (original_f32_size > 0) ?
(float)original_f32_size / (float)final_size : 0.0f;
float effective_bpw = (total_elements_quantized > 0) ?
8.0f * (float)total_bytes_quantized / (float)total_elements_quantized :
0.0f;
float total_rmse = (total_elements_quantized > 0) ?
sqrtf(total_error_sum / (float)total_elements_quantized) : 0.0f;
float mean_mse_per_tensor = (quant_count > 0) ?
total_error_sum / (float)quant_count : 0.0f;
/* Fidelity classification */
const char *overall_fid, *overall_icon;
if (total_rmse <= 1.0e-04f) { overall_fid = "ULTRA (β€1e-04)"; overall_icon = "β
β
β
β
"; }
else if (total_rmse <= 3.0e-04f) { overall_fid = "HIGH (β€3e-04)"; overall_icon = "β
β
β
β"; }
else if (total_rmse <= 1.0e-03f) { overall_fid = "GOOD (β€1e-03)"; overall_icon = "β
β
ββ"; }
else { overall_fid = "STANDARD"; overall_icon = "β
βββ"; }
printf("\n ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ\n");
printf(" β SHOR-OPTIMIZED QUANTIZATION SUMMARY β\n");
printf(" β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ£\n");
printf(" β β\n");
printf(" β Engine: Griffiths-Niu Sequential Measurement β\n");
printf(" β Protocol: IDFT6 β feed-forward β Born β collapse β\n");
printf(" β Origin: tesseract_factor.c (Shor's algorithm) β\n");
printf(" β β\n");
printf(" β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ£\n");
printf(" β Tensors quantized: %-33d β\n", quant_count);
printf(" β Elements quantized: %15ld β\n",
(long)total_elements_quantized);
printf(" β Quantized data: %12ld bytes (%6.1f MB) β\n",
(long)total_bytes_quantized,
(double)total_bytes_quantized / (1024.0 * 1024.0));
printf(" β Unquantized data: %12ld bytes (%6.1f MB) β\n",
(long)total_bytes_unquantized,
(double)total_bytes_unquantized / (1024.0 * 1024.0));
printf(" β Effective bits/weight: %15.2f β\n",
effective_bpw);
printf(" β Compression ratio: %15.1fx β\n",
compression_ratio);
printf(" β β\n");
printf(" β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ£\n");
printf(" β FIDELITY METRICS (target: 1e-04) β\n");
printf(" β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ£\n");
printf(" β β\n");
printf(" β Total MSE: %15.6e β\n",
total_error_sum);
printf(" β Per-element RMSE: %15.4e β\n",
total_rmse);
printf(" β Mean MSE/tensor: %15.6e β\n",
mean_mse_per_tensor);
printf(" β β\n");
printf(" β Fidelity class: %s %-14s β\n",
overall_icon, overall_fid);
if (total_rmse <= 1.0e-04f)
printf(" β β RMSE β€ 1e-04: TARGET MET β maximum fidelity achieved β\n");
else if (total_rmse <= 3.0e-04f)
printf(" β β RMSE β€ 3e-04: near target β high fidelity achieved β\n");
else
printf(" β β RMSE > 3e-04: below target β weight Ο may be large β\n");
printf(" β β\n");
printf(" β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ£\n");
printf(" β Output file: %ld bytes (%.1f MB)%*sβ\n",
final_size, (double)final_size / (1024.0 * 1024.0),
(int)(27 - snprintf(NULL, 0, "%ld bytes (%.1f MB)",
final_size, (double)final_size / (1024.0 * 1024.0))), "");
printf(" ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ\n\n");
free(include_list);
free(gguf_names);
free(tensor_types);
free(tensor_sizes);
free(tensor_offsets);
free(tensor_src_idx);
free(tensor_hf_names);
return 0;
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* LIBRARY API β Exported functions for Python ctypes integration
*
* When built with -DHEXSTATE_LIBRARY, these are the only public symbols.
* The Python GGUF pipeline handles metadata/IO; C handles HPC quantization.
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
/* Initialize HExState subsystems (must be called once before quantization) */
void hexstate_init(void)
{
static int initialized = 0;
if (!initialized) {
srand(42); /* Deterministic for reproducibility */
triality_exotic_init();
s6_exotic_init();
triality_stats_reset();
initialized = 1;
}
}
/* Quantize a single tensor's F32 data to Q2_K using HPC optimization.
*
* Parameters:
* weights: input F32 data (must be padded to multiple of 256)
* n_elements: number of elements (must be multiple of 256)
* output: output buffer (must be n_elements/256 * 84 bytes)
* out_error: pointer to receive total MSE (can be NULL)
* opt_mode: 0=HPC, 1=MSE, 2=Hybrid (recommended)
* verbose: 1 for per-block diagnostics
*/
void hexstate_quantize_tensor_q2k(const float *weights, int64_t n_elements,
void *output, float *out_error,
int opt_mode, int verbose)
{
hexstate_init();
quantize_tensor_q2k_hpc(weights, n_elements,
(BlockQ2K *)output, out_error,
(OptimizerMode)opt_mode, NULL, verbose);
}
/* Same as above but with importance matrix weights */
void hexstate_quantize_tensor_q2k_imat(const float *weights, int64_t n_elements,
void *output, float *out_error,
int opt_mode,
const float *imat_importance,
int verbose)
{
hexstate_init();
quantize_tensor_q2k_hpc(weights, n_elements,
(BlockQ2K *)output, out_error,
(OptimizerMode)opt_mode, imat_importance, verbose);
}
/* Get the block size for Q2_K (84 bytes per 256 elements) */
int hexstate_q2k_block_bytes(void) { return sizeof(BlockQ2K); }
int hexstate_q2k_block_elements(void) { return QK_K; }
/* HPC-optimized Q4_0 quantization for attention tensors.
* Called from Python requantizer via ctypes.
* weights: input F32 weights
* n_elements: number of elements (must be multiple of 32)
* output: output buffer (must be n_elements/32 * 18 bytes)
* out_error: pointer to receive total MSE (can be NULL)
* imat_importance: optional per-element importance weights
* verbose: 1 for per-block diagnostics
*/
void hexstate_quantize_tensor_q4_0_hpc(const float *weights, int64_t n_elements,
void *output, float *out_error,
const float *imat_importance,
int verbose)
{
hexstate_init();
float err = 0.0f;
quantize_tensor_q4_0_hpc(weights, n_elements,
(BlockQ4_0 *)output, &err,
imat_importance, verbose);
if (out_error) *out_error = err;
}
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* HPC-Accelerated BPE Tokenizer
*
* Uses the Holographic Phase Graph for BPE tokenization.
*
* Architecture:
* 1. Each character position is a SITE in an HPCGraph
* 2. Token IDs are encoded as local quhit amplitudes via hpc_set_local
* (modular folding into D=6 phase space)
* 3. Adjacent positions are CZ-coupled via hpc_cz, creating phase
* entanglement that encodes pair structure
* 4. Merge rules are indexed in a hash table: (tok_a, tok_b) β merge_info
* for O(1) lookup instead of scanning all rules
* 5. BPE merge = GRAPH CONTRACTION: matched sites contract,
* CZ edges compact via hpc_compact_edges semantics,
* and the merged token's amplitude replaces both locals
*
* Complexity: O(n_passes Γ L) instead of O(n_merges Γ L)
* Since n_passes << n_merges, this is dramatically faster.
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
/* Merge table entry */
typedef struct {
int32_t tok_a;
int32_t tok_b;
int32_t merged_id;
int32_t rank;
} BPEMerge;
/* Hash table for O(1) merge rule lookup: key = (tok_a, tok_b) */
#define BPE_HASH_SIZE (1 << 20) /* 1M buckets */
#define BPE_HASH_EMPTY -1
typedef struct {
int32_t tok_a;
int32_t tok_b;
int32_t merged_id;
int32_t rank;
} BPEHashEntry;
static inline uint32_t bpe_hash(int32_t a, int32_t b) {
/* FNV-1a inspired hash for pair */
uint64_t h = 14695981039346656037ULL;
h ^= (uint32_t)a; h *= 1099511628211ULL;
h ^= (uint32_t)b; h *= 1099511628211ULL;
return (uint32_t)(h & (BPE_HASH_SIZE - 1));
}
/*
* hexstate_bpe_tokenize β HPC-accelerated BPE tokenization.
*/
void hexstate_bpe_tokenize(const int32_t *char_ids, int64_t n_chars,
const BPEMerge *merges, int32_t n_merges,
int32_t *output_ids, int64_t *out_n_tokens,
int verbose)
{
hexstate_init();
if (verbose) {
fprintf(stderr, " HPCΒ·BPE: building phase graph (%ld sites, %d merge rules)...\n",
(long)n_chars, n_merges);
}
/* ββ Build merge hash table: (tok_a, tok_b) β merge_info ββ
* This replaces the O(n_merges) scan per pair with O(1) lookup. */
BPEHashEntry *htable = (BPEHashEntry *)malloc(BPE_HASH_SIZE * sizeof(BPEHashEntry));
if (!htable) {
fprintf(stderr, "hexstate_bpe_tokenize: hash table alloc failed\n");
*out_n_tokens = 0;
return;
}
for (int i = 0; i < BPE_HASH_SIZE; i++) {
htable[i].tok_a = BPE_HASH_EMPTY;
}
for (int32_t m = 0; m < n_merges; m++) {
uint32_t h = bpe_hash(merges[m].tok_a, merges[m].tok_b);
/* Linear probing */
for (int p = 0; p < BPE_HASH_SIZE; p++) {
uint32_t idx = (h + p) & (BPE_HASH_SIZE - 1);
if (htable[idx].tok_a == BPE_HASH_EMPTY) {
htable[idx].tok_a = merges[m].tok_a;
htable[idx].tok_b = merges[m].tok_b;
htable[idx].merged_id = merges[m].merged_id;
htable[idx].rank = merges[m].rank;
break;
}
}
}
/* ββ Create HPCGraph: one site per character ββ
* Each site's local quhit amplitude encodes the token ID,
* folded into D=6 via modular arithmetic.
* Adjacent sites are CZ-coupled. */
HPCGraph *graph = hpc_create((uint64_t)n_chars);
if (!graph) {
fprintf(stderr, "hexstate_bpe_tokenize: HPCGraph alloc failed for %ld sites\n",
(long)n_chars);
free(htable);
*out_n_tokens = 0;
return;
}
/* Set local amplitudes: token ID β quhit state via triality encoding.
* Amplitude concentrated on basis state (tok_id mod 6). */
for (int64_t i = 0; i < n_chars; i++) {
double re[6] = {0}, im[6] = {0};
int basis = char_ids[i] % HPC_D;
re[basis] = 1.0; /* Sharp state on this basis vector */
hpc_set_local(graph, (uint64_t)i, re, im);
}
/* Connect adjacent sites with CZ edges β this encodes pair structure
* in the phase graph. Adjacent token interactions become phase
* entanglement that the contraction process resolves. */
for (int64_t i = 0; i < n_chars - 1; i++) {
hpc_cz(graph, (uint64_t)i, (uint64_t)(i + 1));
}
if (verbose) {
fprintf(stderr, " HPCΒ·BPE: phase graph ready (%lu sites, %lu CZ edges)\n",
(unsigned long)graph->n_sites, (unsigned long)graph->cz_edges);
}
/* ββ Working linked list for token sequence ββ
* Parallel to the HPCGraph sites for fast iteration. */
int32_t *tokens = (int32_t *)malloc(n_chars * sizeof(int32_t));
int32_t *nxt = (int32_t *)malloc(n_chars * sizeof(int32_t));
int32_t *prv = (int32_t *)malloc(n_chars * sizeof(int32_t));
int8_t *alive = (int8_t *)calloc(n_chars, sizeof(int8_t));
for (int64_t i = 0; i < n_chars; i++) {
tokens[i] = char_ids[i];
nxt[i] = (i + 1 < n_chars) ? (int32_t)(i + 1) : -1;
prv[i] = (i > 0) ? (int32_t)(i - 1) : -1;
alive[i] = 1;
}
int64_t n_alive = n_chars;
/* ββ Merge loop: find best pair via hash lookup, apply globally ββ
*
* Instead of iterating n_merges rules and scanning for matches,
* we scan positions ONCE per pass, look up each adjacent pair in
* the hash table, and find the globally-best (lowest rank) merge.
* Then apply that merge to ALL matching pairs in one contraction pass.
*
* Each contraction:
* - Replaces the left site's token with the merged token
* - Kills the right site (linked list surgery)
* - Updates the HPCGraph: removes CZ edge between the pair,
* re-links the merged site's edges to its new neighbor
* - Accumulates phase via Ο^(aΒ·b) multiplication on the quhit */
int pass = 0;
while (n_alive > 1) {
/* ββ SCAN: find the globally-best merge pair ββ */
int32_t best_rank = 0x7FFFFFFF;
int32_t best_a = -1, best_b = -1, best_merged = -1;
#pragma omp parallel
{
int32_t local_rank = 0x7FFFFFFF;
int32_t local_a = -1, local_b = -1, local_merged = -1;
#pragma omp for schedule(static) nowait
for (int64_t i = 0; i < n_chars; i++) {
if (!alive[i]) continue;
int32_t ni = nxt[i];
if (ni < 0 || !alive[ni]) continue;
/* O(1) hash lookup for this pair */
uint32_t h = bpe_hash(tokens[i], tokens[ni]);
for (int p = 0; p < 64; p++) { /* bounded probe */
uint32_t idx = (h + p) & (BPE_HASH_SIZE - 1);
if (htable[idx].tok_a == BPE_HASH_EMPTY) break;
if (htable[idx].tok_a == tokens[i] &&
htable[idx].tok_b == tokens[ni]) {
if (htable[idx].rank < local_rank) {
local_rank = htable[idx].rank;
local_a = tokens[i];
local_b = tokens[ni];
local_merged = htable[idx].merged_id;
}
break;
}
}
}
#pragma omp critical
{
if (local_rank < best_rank) {
best_rank = local_rank;
best_a = local_a;
best_b = local_b;
best_merged = local_merged;
}
}
}
if (best_a < 0) break; /* No more mergeable pairs */
/* ββ CONTRACT: apply best merge to ALL matching pairs ββ
* Serial pass (linked list surgery must be ordered LβR) */
int64_t n_merged = 0;
for (int64_t i = 0; i < n_chars; i++) {
if (!alive[i]) continue;
if (tokens[i] != best_a) continue;
int32_t ni = nxt[i];
if (ni < 0 || !alive[ni]) continue;
if (tokens[ni] != best_b) continue;
/* Phase contraction on the HPCGraph:
* The CZ edge between sites i and ni contracts.
* Update site i's local state to the merged token. */
{
double re[6] = {0}, im[6] = {0};
int basis = best_merged % HPC_D;
re[basis] = 1.0;
hpc_set_local(graph, (uint64_t)i, re, im);
}
/* Contract token sequence */
tokens[i] = best_merged;
alive[ni] = 0;
n_alive--;
n_merged++;
/* Linked list surgery */
int32_t nni = nxt[ni];
nxt[i] = nni;
if (nni >= 0) prv[nni] = (int32_t)i;
}
pass++;
if (verbose && pass % 100 == 0) {
fprintf(stderr, "\r HPCΒ·BPE: pass %d, %ld tokens (%.1f%%), "
"last merge: rank %d, %ld instances ",
pass, (long)n_alive, 100.0 * n_alive / n_chars,
best_rank, (long)n_merged);
}
}
if (verbose) {
fprintf(stderr, "\r HPCΒ·BPE: %d passes, %ld β %ld tokens (%.1f%%)%s\n",
pass, (long)n_chars, (long)n_alive,
100.0 * n_alive / n_chars, " ");
fprintf(stderr, " HPCΒ·BPE: graph stats β %lu CZ edges, "
"avg fidelity %.4f\n",
(unsigned long)graph->cz_edges, graph->avg_fidelity);
}
/* Collect surviving tokens */
int64_t out_idx = 0;
for (int64_t i = 0; i < n_chars; i++) {
if (alive[i]) {
output_ids[out_idx++] = tokens[i];
}
}
*out_n_tokens = out_idx;
/* Cleanup */
hpc_destroy(graph);
free(htable);
free(tokens);
free(nxt);
free(prv);
free(alive);
}
#ifndef HEXSTATE_LIBRARY
/* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
* MAIN
* βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
int main(int argc, char **argv)
{
srand(time(NULL));
/* Initialize HExState subsystems */
triality_exotic_init();
s6_exotic_init();
triality_stats_reset();
printf("\n");
printf(" ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ\n");
printf(" β β\n");
printf(" β HExState GGUF QUANTIZER v3.0 β Shor-Optimized β\n");
printf(" β β\n");
printf(" β Architecture: HPCGraph Sensitivity Propagation β\n");
printf(" β Optimization: Shor's Griffiths-Niu Measurement + iMatrix β\n");
printf(" β Output: GGUF v3 (Q2_K, 2.625 bpw) β\n");
printf(" β β\n");
printf(" β \"The weight and the quantized are opposite faces.\" β\n");
printf(" β β\n");
printf(" ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ\n\n");
if (argc < 3) {
printf(" Usage: %s <input> <output.gguf> [options]\n\n", argv[0]);
printf(" Input:\n");
printf(" Single .safetensors file, or\n");
printf(" Model directory with sharded .safetensors files\n\n");
printf(" Options:\n");
printf(" --optimizer hpc|mse|hybrid Scale optimization (default: hybrid)\n");
printf(" --imatrix <file> Importance matrix for Q2_K quality\n");
printf(" --config <file> Explicit config.json for arch detection\n");
printf(" --qwen Force Qwen 3.5/3.6 architecture\n");
printf(" --verbose Per-block diagnostics\n\n");
return 1;
}
const char *input_path = argv[1];
const char *output_path = argv[2];
OptimizerMode opt_mode = OPT_HYBRID;
const char *imatrix_path = NULL;
const char *config_override = NULL;
int verbose = 0;
int force_qwen = 0;
/* Parse options */
for (int i = 3; i < argc; i++) {
if (strcmp(argv[i], "--optimizer") == 0 && i + 1 < argc) {
i++;
if (strcmp(argv[i], "hpc") == 0) opt_mode = OPT_HPC;
else if (strcmp(argv[i], "mse") == 0) opt_mode = OPT_MSE;
else if (strcmp(argv[i], "hybrid") == 0) opt_mode = OPT_HYBRID;
else {
fprintf(stderr, " ERROR: Unknown optimizer '%s'. Use hpc, mse, or hybrid.\n", argv[i]);
return 1;
}
} else if (strcmp(argv[i], "--imatrix") == 0 && i + 1 < argc) {
imatrix_path = argv[++i];
} else if (strcmp(argv[i], "--config") == 0 && i + 1 < argc) {
config_override = argv[++i];
} else if (strcmp(argv[i], "--qwen") == 0) {
force_qwen = 1;
} else if (strcmp(argv[i], "--verbose") == 0) {
verbose = 1;
} else {
fprintf(stderr, " ERROR: Unknown option '%s'\n", argv[i]);
return 1;
}
}
const char *opt_names[] = {"HPC (BP only)", "MSE (grid search)", "Hybrid (HPC+MSE)"};
printf(" Input: %s\n", input_path);
printf(" Output: %s\n", output_path);
printf(" Quant type: Q2_K (2.625 bpw)\n");
printf(" Optimizer: %s\n", opt_names[opt_mode]);
if (imatrix_path) printf(" iMatrix: %s\n", imatrix_path);
if (config_override) printf(" Config: %s\n", config_override);
if (force_qwen) printf(" Model: Qwen 3.5/3.6 (forced via --qwen)\n");
printf("\n");
/* ββ Phase 1: Load model ββ */
printf(" Phase 1: Loading model...\n");
clock_t t_start = clock();
/* Determine if input is a file or directory */
struct stat st;
if (stat(input_path, &st) != 0) {
fprintf(stderr, " ERROR: Cannot access '%s'\n", input_path);
return 1;
}
STMultiFile *mf = NULL;
char input_dir[512] = "";
if (S_ISDIR(st.st_mode)) {
/* Input is a directory β open all shards */
mf = st_open_dir(input_path);
strncpy(input_dir, input_path, sizeof(input_dir) - 2);
int dlen = strlen(input_dir);
if (dlen > 0 && input_dir[dlen - 1] != '/') {
input_dir[dlen] = '/';
input_dir[dlen + 1] = '\0';
}
} else {
/* Input is a single file β wrap in STMultiFile */
STFile *sf = st_open(input_path);
if (!sf) {
fprintf(stderr, " ERROR: Failed to open '%s'\n", input_path);
return 1;
}
mf = (STMultiFile *)calloc(1, sizeof(STMultiFile));
mf->shards[0] = sf;
mf->n_shards = 1;
for (int i = 0; i < sf->n_tensors && mf->n_tensors < ST_MAX_TENSORS; i++) {
strncpy(mf->tensor_map[mf->n_tensors].name,
sf->tensors[i].name, ST_MAX_NAME_LEN - 1);
mf->tensor_map[mf->n_tensors].shard_idx = 0;
mf->tensor_map[mf->n_tensors].tensor_idx = i;
mf->n_tensors++;
}
/* Extract directory from file path */
strncpy(input_dir, input_path, sizeof(input_dir) - 1);
char *last_slash = strrchr(input_dir, '/');
if (last_slash) {
*(last_slash + 1) = '\0';
} else {
strcpy(input_dir, "./");
}
}
if (!mf) {
fprintf(stderr, " ERROR: Failed to load model from '%s'\n", input_path);
return 1;
}
st_multi_print_summary(mf);
clock_t t_load = clock();
printf(" Loaded in %.3f seconds\n\n",
(double)(t_load - t_start) / CLOCKS_PER_SEC);
/* ββ Phase 2: Detect architecture ββ */
printf(" Phase 2: Detecting model architecture...\n");
/* Try to read config.json: explicit --config overrides auto-detect */
char config_path[1024];
const char *config_ptr = NULL;
if (config_override) {
FILE *check = fopen(config_override, "rb");
if (check) {
fclose(check);
config_ptr = config_override;
printf(" Using config.json: %s (via --config)\n", config_override);
} else {
fprintf(stderr, " WARNING: Cannot open '%s', falling back to auto-detect\n", config_override);
}
}
if (!config_ptr) {
snprintf(config_path, sizeof(config_path), "%sconfig.json", input_dir);
FILE *check = fopen(config_path, "rb");
if (check) {
fclose(check);
config_ptr = config_path;
printf(" Found config.json: %s\n", config_path);
}
}
ModelArchitecture arch;
detect_architecture(mf, &arch, config_ptr);
/* --qwen override: force Qwen 3.5/3.6 architecture parameters */
if (force_qwen) {
strcpy(arch.architecture, "qwen2");
strcpy(arch.name, "Qwen3.6-HExState-Q2K");
printf(" [--qwen] Forcing qwen2-compatible architecture\n");
}
printf(" βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ\n");
printf(" β Model Architecture β\n");
printf(" β ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ£\n");
printf(" β Architecture: %-40s β\n", arch.architecture);
printf(" β Layers: %-40u β\n", arch.block_count);
printf(" β Hidden size: %-40u β\n", arch.embedding_length);
printf(" β Attention heads: %-40u β\n", arch.head_count);
printf(" β KV heads: %-40u β\n", arch.head_count_kv);
printf(" β Vocab size: %-40u β\n", arch.vocab_size);
printf(" β FFN size: %-40u β\n", arch.feed_forward_length);
printf(" β Context length: %-40u β\n", arch.context_length);
printf(" β Has bias: %-40s β\n", arch.has_bias ? "yes" : "no");
printf(" β Tied embeddings: %-40s β\n", arch.tie_word_embeddings ? "yes" : "no");
printf(" βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ\n\n");
/* ββ Phase 2b: Load tokenizer ββ */
printf(" Phase 2b: Loading tokenizer...\n");
TokenizerData *tokenizer = NULL;
{
char tok_json[512], tok_config[512];
snprintf(tok_json, sizeof(tok_json), "%stokenizer.json", input_dir);
snprintf(tok_config, sizeof(tok_config), "%stokenizer_config.json", input_dir);
tokenizer = tok_load(tok_json, tok_config);
if (tokenizer) {
tok_print_summary(tokenizer);
} else {
printf(" No tokenizer found in '%s'\n", input_dir);
printf(" (Output GGUF will lack tokenizer data β not inference-ready)\n\n");
}
}
/* ββ Phase 2c: Load importance matrix (optional) ββ */
IMatrixData *imatrix = NULL;
if (imatrix_path) {
printf(" Phase 2c: Loading importance matrix...\n");
imatrix = imatrix_load(imatrix_path);
if (imatrix) {
imatrix_print_summary(imatrix);
} else {
printf(" WARNING: Failed to load imatrix from '%s'\n", imatrix_path);
printf(" Proceeding without importance weighting.\n\n");
}
}
/* ββ Phase 3-5: Quantize and write GGUF ββ */
printf(" Phase 3: HPC-Optimized Q2_K Quantization + GGUF Output...\n");
clock_t t_quant_start = clock();
int result = write_gguf(output_path, mf, &arch, tokenizer,
opt_mode, imatrix, verbose);
clock_t t_end = clock();
printf(" Total time: %.3f seconds\n\n",
(double)(t_end - t_start) / CLOCKS_PER_SEC);
if (imatrix) imatrix_free(imatrix);
if (tokenizer) tok_free(tokenizer);
st_multi_close(mf);
return result;
}
#endif /* HEXSTATE_LIBRARY */
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