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Pipeline stages (mirroring the C engine, with V2 fixes):
1. kurtosis-gated outlier clamp and batched GGML seeds;
2. vectorised WLS superblock solve;
3. factored candidate field + standalone factored floor;
4. sequential Sieve over coarse/fine symbolic states;
5. 36-state Viterbi sequence optimisation;
6. candidate-aware factor consistency pass;
7. rolling DC residual carry;
8. sub-block Sieve assembly, LS refit, fp16-ULP polish, spectral shaping;
9. objective-consistent local-safety floors and DC nudge.
"""
from __future__ import annotations
from dataclasses import dataclass
from math import ceil
import multiprocessing as mp
import os
import time
import numpy as np
from .anneal import thermal_refine_tensor
from .config import HPCConfig
from .factored import (
CAND_TO_QUHIT,
MULTS_D,
MULTS_M,
N_CAND_M,
TOTAL_CANDIDATES,
FactorResult,
candidate_pair,
factor_candidates,
factor_only_solve,
factor_q2k_block,
)
from .fp16 import from_fp16_bits, nearest_int, round_fp16, to_fp16_bits
from .q2k import (
NS,
QK_K,
Q2KBlock,
codes_from_params,
codes_from_params_batch,
dequantize_params,
dequantize_params_batch,
derive_subscales,
make_block,
pack_codes,
pack_scales,
pin_q2k_dc_residual,
unpack_codes,
weighted_sse,
)
from .seeds import block_clamp_values, build_seeds, seed_clamp_value
from .sieve import block_chain_edges, sieve_measure_chain, subblock_chain_edges
from .spectral import dc_nudge_codes, fold_weights, objective, refine_codes_hadamard
from .viterbi import robust_temperature, viterbi_select
@dataclass(slots=True)
class TensorStats:
n_blocks: int
total_sse: float
weighted_sse: float
rmse: float
sigma: float
rmse_over_sigma: float
temperature: float
factor_floor_used: int
candidate_floor_used: int
therm_improved: int = 0
@dataclass(slots=True)
class QuantizedTensor:
blocks: list[Q2KBlock]
scales: np.ndarray
qs: np.ndarray
dm: np.ndarray
best_candidate: np.ndarray
factor_objectives: np.ndarray
config: HPCConfig
stats: TensorStats
def dequantize(self) -> np.ndarray:
return np.concatenate([b.dequantize() for b in self.blocks])
def _phase2_wls(X, W, seeds, cfg: HPCConfig):
n = X.shape[0]
per_blk_clamp = block_clamp_values(X, cfg).reshape(n, 1)
clipped = np.clip(X, -per_blk_clamp, per_blk_clamp).astype(np.float32)
base_d = seeds["d"].astype(np.float64).copy()
base_m = seeds["dmin"].astype(np.float64).copy()
ls = seeds["ls"].copy()
lm = seeds["lm"].copy()
scales = seeds["scales"].astype(np.float64)
mins = seeds["mins"].astype(np.float64)
ww = np.ones_like(X, dtype=np.float64) if W is None else W.astype(np.float64)
for _ in range(5):
codes = codes_from_params_batch(clipped, base_d, base_m, ls, lm).astype(np.float64)
A = np.repeat(ls.astype(np.float64), 16, axis=1) * codes
B = np.repeat(lm.astype(np.float64), 16, axis=1)
Saa = np.sum(ww * A * A, axis=1)
Sab = np.sum(ww * A * B, axis=1)
Sbb = np.sum(ww * B * B, axis=1)
Sxa = np.sum(ww * clipped.astype(np.float64) * A, axis=1)
Sxb = np.sum(ww * clipped.astype(np.float64) * B, axis=1)
det = Saa * Sbb - Sab * Sab
valid = det > 1.0e-30
safe_det = np.where(valid, det, 1.0)
d_new = (Sbb * Sxa - Sab * Sxb) / safe_det
m_new = (Sab * Sxa - Saa * Sxb) / safe_det
upd_d = valid & (d_new > 0.0) & np.isfinite(d_new)
upd_m = valid & (m_new > 0.0) & np.isfinite(m_new)
base_d = np.where(upd_d, round_fp16(d_new.astype(np.float32)).astype(np.float64), base_d)
base_m = np.where(upd_m, round_fp16(m_new.astype(np.float32)).astype(np.float64), base_m)
safe_d = np.where(base_d > 1.0e-15, base_d, 1.0)
safe_m = np.where(base_m > 1.0e-15, base_m, 1.0)
ls = np.where(
base_d[:, None] > 1.0e-15,
np.clip(nearest_int(scales / safe_d[:, None]), 0, 15),
0,
).astype(np.uint8)
lm = np.where(
base_m[:, None] > 1.0e-15,
np.clip(nearest_int(mins / safe_m[:, None]), 0, 15),
0,
).astype(np.uint8)
return base_d.astype(np.float32), base_m.astype(np.float32)
def _bin_geometry():
di = np.arange(TOTAL_CANDIDATES) // N_CAND_M
mi = np.arange(TOTAL_CANDIDATES) % N_CAND_M
return CAND_TO_QUHIT[di], CAND_TO_QUHIT[mi]
def _sieve_marginals(candidate_errors, graph_blocks, stride, temperature, cfg):
n_blocks = candidate_errors.shape[0]
pad = graph_blocks * stride - n_blocks
ce = candidate_errors.astype(np.float64)
if pad > 0:
ce = np.concatenate([ce, np.full((pad, TOTAL_CANDIDATES), np.inf, dtype=np.float64)], axis=0)
grouped = ce.reshape(graph_blocks, stride, TOTAL_CANDIDATES)
finite = np.isfinite(grouped)
counts = np.sum(finite, axis=1) # (graph_blocks, 576)
valid = counts > 0
sums = np.sum(np.where(finite, grouped, 0.0), axis=1)
agg = np.where(valid, sums / np.maximum(counts, 1), np.inf)
mn = np.min(agg, axis=1, keepdims=True)
mn = np.where(np.isfinite(mn), mn, 0.0)
expo = np.where(valid, (agg - mn) / (2.0 * max(temperature, 1.0e-12)), np.inf)
coeff = np.exp(-expo).reshape(graph_blocks, 6, 4, 6, 4)
coarse = np.sum(coeff, axis=(2, 3, 4)) # (graph_blocks, 6)
fine = np.sum(coeff, axis=(1, 2, 4)) # (graph_blocks, 6)
cn = np.linalg.norm(coarse, axis=1, keepdims=True)
fn = np.linalg.norm(fine, axis=1, keepdims=True)
coarse = np.where(cn > 1.0e-30, coarse / np.maximum(cn, 1.0e-30), 0.0)
fine = np.where(fn > 1.0e-30, fine / np.maximum(fn, 1.0e-30), 0.0)
amps = np.zeros((2 * graph_blocks, 6), dtype=np.float64)
amps[0::2] = coarse
amps[1::2] = fine
marg, _ = sieve_measure_chain(
amps,
block_chain_edges(graph_blocks),
same_penalty=cfg.same_penalty,
log_slack=cfg.log_slack,
deterministic=True,
)
return marg[0::2], marg[1::2]
def _phase3_select(candidate_errors, cfg):
n_blocks = candidate_errors.shape[0]
if cfg.optimizer != "hpc" or n_blocks < 2:
best = np.argmin(candidate_errors, axis=1).astype(np.int64)
return best, 0.0, np.zeros(n_blocks, dtype=np.int64)
stride = max(1, ceil(n_blocks / cfg.graph_max_blocks))
graph_blocks = ceil(n_blocks / stride)
temperature = robust_temperature(candidate_errors, cfg)
coarse_marg, fine_marg = _sieve_marginals(
candidate_errors, graph_blocks, stride, temperature, cfg
)
best, path = viterbi_select(
candidate_errors,
coarse_marg,
fine_marg,
temperature,
stride,
graph_blocks,
cfg,
)
return best, temperature, path
def _compute_dc_carry(X, seeds, best, base_d, base_m, cfg, row_width):
n = X.shape[0]
carry = np.zeros(n, dtype=np.float32)
blocks_per_row = row_width // QK_K if row_width and row_width % QK_K == 0 else 0
rolling = 0.0
for i in range(n):
if blocks_per_row > 0 and i % blocks_per_row == 0:
rolling = 0.0
d16, m16 = candidate_pair(base_d[i], base_m[i], best[i])
d = float(from_fp16_bits(d16))
m = float(from_fp16_bits(m16))
ls, lm = derive_subscales(seeds["scales"][i], seeds["mins"][i], d, m)
codes = codes_from_params(X[i], d, m, ls, lm)
xh = dequantize_params(d, m, ls, lm, codes)
carry[i] = np.float32(cfg.dc_decay * rolling)
dc_res = float(np.sum(X[i].astype(np.float64) - xh.astype(np.float64)))
rolling = carry[i] + dc_res if cfg.carry_cumulative else dc_res
return carry
def _per_sub_top_states(x, w, d, m, k=6):
k = max(1, min(int(k), 6))
xb = np.asarray(x, dtype=np.float32).reshape(NS, 16)
if w is None:
wb = np.ones((NS, 16), dtype=np.float32)
else:
wb = np.asarray(w, dtype=np.float32).reshape(NS, 16)
ls_opts = np.arange(16, dtype=np.float32)[:, None]
lm_opts = np.arange(16, dtype=np.float32)[None, :]
d_sub = np.float32(d) * ls_opts
m_sub = np.float32(m) * lm_opts
safe = d_sub >= np.float32(1.0e-15)
safe_d = np.where(safe, d_sub, np.float32(1.0))
subs = xb[:, None, None, :]
raw = (subs + m_sub[None, :, :, None]) / safe_d[None, :, :, None]
q = np.clip(nearest_int(raw), 0, 3).astype(np.float32)
q = np.where(safe[None, :, :, None], q, 0.0)
deq = d_sub[None, :, :, None] * q - m_sub[None, :, :, None]
err = np.sum(wb[:, None, None, :] * (subs - deq) ** 2, axis=3)
flat = err.reshape(NS, 256)
order = np.argsort(flat, axis=1, kind="stable")[:, :k]
state_err = np.take_along_axis(flat, order, axis=1).astype(np.float64)
state_ls = (order // 16).astype(np.uint8)
state_lm = (order % 16).astype(np.uint8)
return state_ls, state_lm, state_err
def _select_sub_states(state_ls, state_lm, state_err, cfg):
if not cfg.subblock_sieve:
return state_ls[:, 0].copy(), state_lm[:, 0].copy()
amps = np.zeros((NS, 6), dtype=np.float64)
for j in range(NS):
err0 = state_err[j, 0]
spread = float(state_err[j, 5] - state_err[j, 0])
temp = spread * 0.3 if spread > 1.0e-15 else 0.1
temp = max(temp, 1.0e-12)
amp = np.exp(-(state_err[j] - err0) / temp)
norm = float(np.linalg.norm(amp))
amps[j] = amp / norm if norm > 1.0e-30 else 1.0 / np.sqrt(6.0)
marg, _ = sieve_measure_chain(
amps,
subblock_chain_edges(NS),
same_penalty=cfg.same_penalty,
log_slack=cfg.log_slack,
deterministic=True,
)
pick = np.argmax(marg, axis=1)
ls = np.array([state_ls[j, pick[j]] for j in range(NS)], dtype=np.uint8)
lm = np.array([state_lm[j, pick[j]] for j in range(NS)], dtype=np.uint8)
return ls, lm
def _ls_refit(x, w, d, m, ls, lm, codes, max_d, max_m):
x = np.asarray(x, dtype=np.float64)
w = np.ones(QK_K, dtype=np.float64) if w is None else np.asarray(w, dtype=np.float64)
A = np.repeat(np.asarray(ls, dtype=np.float64), 16) * np.asarray(codes, dtype=np.float64)
B = np.repeat(np.asarray(lm, dtype=np.float64), 16)
Saa = float(np.sum(w * A * A))
Sab = float(np.sum(w * A * B))
Sbb = float(np.sum(w * B * B))
Sxa = float(np.sum(w * x * A))
Sxb = float(np.sum(w * x * B))
det = Saa * Sbb - Sab * Sab
if det <= 1.0e-30:
return float(d), float(m)
d_new = (Sbb * Sxa - Sab * Sxb) / det
m_new = (Sab * Sxa - Saa * Sxb) / det
if d_new <= 0.0 or d_new >= 4.0 * (max_d + 1.0e-10):
d_new = d
if m_new <= 0.0 or m_new >= 4.0 * (max_m + 1.0e-10):
m_new = m
return float(round_fp16(np.float32(d_new))), float(round_fp16(np.float32(m_new)))
def _ulp_polish(x, w, d, m, ls, lm, radius):
if radius <= 0:
codes = codes_from_params(x, d, m, ls, lm)
return float(d), float(m), codes, weighted_sse(x, w, d, m, ls, lm, codes)
d16 = int(to_fp16_bits(np.float32(d)))
m16 = int(to_fp16_bits(np.float32(m)))
d_bits = np.arange(max(0, d16 - radius), min(0x7BFF, d16 + radius) + 1, dtype=np.uint16)
m_bits = np.arange(max(0, m16 - radius), min(0x7BFF, m16 + radius) + 1, dtype=np.uint16)
dd = from_fp16_bits(d_bits)
mm = from_fp16_bits(m_bits)
d_grid = np.repeat(dd, mm.size)
m_grid = np.tile(mm, dd.size)
ls_rep = np.repeat(np.asarray(ls, dtype=np.float32), 16)
lm_rep = np.repeat(np.asarray(lm, dtype=np.float32), 16)
d_sub = d_grid[:, None] * ls_rep[None, :]
m_sub = m_grid[:, None] * lm_rep[None, :]
safe = d_sub >= np.float32(1.0e-15)
safe_d = np.where(safe, d_sub, np.float32(1.0))
q = np.clip(nearest_int((x[None, :] + m_sub) / safe_d), 0, 3).astype(np.float32)
q = np.where(safe, q, 0.0)
deq = d_sub * q - m_sub
ww = np.ones(QK_K, dtype=np.float64) if w is None else np.asarray(w, dtype=np.float64)
err = np.sum(ww[None, :] * (np.asarray(x, dtype=np.float64)[None, :] - deq) ** 2, axis=1)
k = int(np.argmin(err))
codes = q[k].astype(np.uint8)
return float(d_grid[k]), float(m_grid[k]), codes, float(err[k])
def _per_sub_scan(x, w, d, m):
state_ls, state_lm, _ = _per_sub_top_states(x, w, d, m, k=1)
return state_ls[:, 0], state_lm[:, 0]
def _assemble_block(x, w, seeds, base_d, base_m, cidx, dc_carry, cfg):
d16, m16 = candidate_pair(base_d, base_m, cidx)
d = float(from_fp16_bits(d16))
m = float(from_fp16_bits(m16))
ls, lm = derive_subscales(seeds["scales"], seeds["mins"], d, m)
max_d, max_m = d, m
prev = None
for _ in range(5):
s_ls, s_lm, s_err = _per_sub_top_states(x, w, d, m, cfg.subblock_states)
ls, lm = _select_sub_states(s_ls, s_lm, s_err, cfg)
codes = codes_from_params(x, d, m, ls, lm)
d, m = _ls_refit(x, w, d, m, ls, lm, codes, max_d, max_m)
cur = (int(to_fp16_bits(np.float32(d))), int(to_fp16_bits(np.float32(m))))
if cur == prev:
break
prev = cur
codes = codes_from_params(x, d, m, ls, lm)
obj, sse = objective(x, w, d, m, ls, lm, codes, cfg, dc_carry)
best = (obj, sse, d, m, ls.copy(), lm.copy(), codes.copy())
ud, um, uc, _ = _ulp_polish(x, w, d, m, ls, lm, cfg.ulp_radius)
uobj, usse = objective(x, w, ud, um, ls, lm, uc, cfg, dc_carry)
if uobj < best[0] - 1.0e-15:
best = (uobj, usse, ud, um, ls.copy(), lm.copy(), uc.copy())
sls, slm = _per_sub_scan(x, w, ud, um)
scodes = codes_from_params(x, ud, um, sls, slm)
sobj, ssse = objective(x, w, ud, um, sls, slm, scodes, cfg, dc_carry)
if sobj < best[0] - 1.0e-15:
best = (sobj, ssse, ud, um, sls.copy(), slm.copy(), scodes.copy())
_, _, bd, bm, bls, blm, bc = best
refined = refine_codes_hadamard(x, w, bd, bm, bls, blm, bc, cfg, dc_carry)
robj, rsse = objective(x, w, bd, bm, bls, blm, refined, cfg, dc_carry)
if robj < best[0] - 1.0e-15:
best = (robj, rsse, bd, bm, bls.copy(), blm.copy(), refined.copy())
_, sse, d, m, ls, lm, codes = best
return d, m, ls, lm, codes, best[0], sse
def _batch_objective(
X: np.ndarray,
W_f64: np.ndarray,
d: np.ndarray,
m: np.ndarray,
ls: np.ndarray,
lm: np.ndarray,
codes: np.ndarray,
carry: np.ndarray,
cfg: HPCConfig,
) -> tuple[np.ndarray, np.ndarray]:
xh = dequantize_params_batch(d, m, ls, lm, codes).astype(np.float64)
e = X.astype(np.float64) - xh
sse = np.sum(W_f64 * e * e, axis=1)
if not cfg.use_spectral or (cfg.dc_lambda == 0.0 and cfg.vw_lambda == 0.0):
return sse, sse
fw = fold_weights(QK_K, cfg)
pen = np.zeros(X.shape[0], dtype=np.float64)
src = e
m_len = QK_K // 2
lvl = 0
while m_len >= 1:
dst = src[:, :m_len] + src[:, m_len : 2 * m_len]
if m_len == 1:
dst = dst + carry[:, None].astype(np.float64)
if fw[lvl] != 0.0:
pen += fw[lvl] * np.sum(dst * dst, axis=1)
src = dst
m_len //= 2
lvl += 1
return sse + pen, sse
def _batch_extended_dm_solve_and_ulp(
X_f64: np.ndarray,
W_f64: np.ndarray,
ls: np.ndarray,
lm: np.ndarray,
codes: np.ndarray,
d_cur: np.ndarray,
m_cur: np.ndarray,
carry: np.ndarray,
cfg: HPCConfig,
) -> tuple[np.ndarray, np.ndarray]:
"""Closed-form 2x2 Cramer solve + O(1) quadratic ULP polish on true weighted SSE."""
N = X_f64.shape[0]
A = np.repeat(ls.astype(np.float64), 16, axis=1) * codes.astype(np.float64)
B = np.repeat(lm.astype(np.float64), 16, axis=1)
Saa = np.sum(W_f64 * A * A, axis=1)
Sab = np.sum(W_f64 * A * B, axis=1)
Sbb = np.sum(W_f64 * B * B, axis=1)
Sxa = np.sum(W_f64 * X_f64 * A, axis=1)
Sxb = np.sum(W_f64 * X_f64 * B, axis=1)
det = Saa * Sbb - Sab * Sab
valid = det > 1.0e-30
s_det = np.where(valid, det, 1.0)
d_new = (Sbb * Sxa - Sab * Sxb) / s_det
m_new = (Sab * Sxa - Saa * Sxb) / s_det
d_opt = np.where(valid & (d_new > 0.0) & np.isfinite(d_new), d_new, d_cur)
m_opt = np.where(valid & (m_new >= 0.0) & np.isfinite(m_new), m_new, m_cur)
rad = max(int(cfg.ulp_radius), 1)
deltas = np.arange(-rad, rad + 1, dtype=np.int32)
d16_c = to_fp16_bits(d_opt.astype(np.float32)).astype(np.int32)
m16_c = to_fp16_bits(m_opt.astype(np.float32)).astype(np.int32)
d_bits = np.clip(d16_c[:, None] + deltas[None, :], 0, 0x7BFF).astype(np.uint16)
m_bits = np.clip(m16_c[:, None] + deltas[None, :], 0, 0x7BFF).astype(np.uint16)
dd = from_fp16_bits(d_bits).astype(np.float64)[:, :, None]
mm = from_fp16_bits(m_bits).astype(np.float64)[:, None, :]
q_form = (
Saa[:, None, None] * (dd * dd)
- 2.0 * Sab[:, None, None] * (dd * mm)
+ Sbb[:, None, None] * (mm * mm)
- 2.0 * Sxa[:, None, None] * dd
+ 2.0 * Sxb[:, None, None] * mm
).reshape(N, -1)
best_idx = np.argmin(q_form, axis=1)
K = deltas.size
rows = np.arange(N)
return dd[rows, best_idx // K, 0].astype(np.float32), mm[rows, 0, best_idx % K].astype(np.float32)
def _batch_algebraic_refine_codes(
X_f64: np.ndarray,
W_f64: np.ndarray,
d: np.ndarray,
m: np.ndarray,
ls: np.ndarray,
lm: np.ndarray,
codes: np.ndarray,
carry: np.ndarray,
cfg: HPCConfig,
) -> np.ndarray:
"""Closed-form 1D quadratic coordinate projection for Vesica + DC + weighted SSE."""
N = X_f64.shape[0]
out = codes.astype(np.int64).copy()
lam_vw = (float(cfg.vw_lambda) / QK_K) if cfg.use_spectral else 0.0
lam_dc = (float(cfg.dc_lambda) / QK_K) if cfg.use_spectral else 0.0
if lam_vw == 0.0 and lam_dc == 0.0:
return out.astype(np.uint8)
d_sub = np.repeat(d.astype(np.float64)[:, None] * ls.astype(np.float64), 16, axis=1)
m_sub = np.repeat(m.astype(np.float64)[:, None] * lm.astype(np.float64), 16, axis=1)
active = d_sub >= 1.0e-15
e = X_f64 - (d_sub * out - m_sub)
sse_live = np.sum(W_f64 * e * e, axis=1)
sse_cap = sse_live * (1.0 + cfg.sse_budget)
half = QK_K // 2
v = e[:, :half] + e[:, half:]
dc = np.sum(e, axis=1) + carry.astype(np.float64)
denom = (W_f64 + lam_vw + lam_dc) * (d_sub * d_sub)
safe_den = np.where(active & (denom > 1.0e-30), denom, 1.0)
v_full = np.concatenate([v, v], axis=1)
grad = d_sub * (W_f64 * e + lam_vw * v_full + lam_dc * dc[:, None])
dq = np.where(active, nearest_int((grad / safe_den).astype(np.float32)), 0)
q_new = np.clip(out + dq, 0, 3)
for b in range(N):
changed = np.where(q_new[b] != out[b])[0]
if changed.size == 0:
continue
xb, wb, dsb, msb = X_f64[b], W_f64[b], d_sub[b], m_sub[b]
eb, vb, dcb, sseb, capb = e[b], v[b], float(dc[b]), float(sse_live[b]), float(sse_cap[b])
e_cand = xb[changed] - (dsb[changed] * q_new[b, changed] - msb[changed])
de_cand = e_cand - eb[changed]
dsse_cand = wb[changed] * (e_cand * e_cand - eb[changed] * eb[changed])
pi_cand = np.where(changed < half, changed, changed - half)
v_old = vb[pi_cand]
v_new = v_old + de_cand
d_spec = lam_vw * (v_old * v_old - v_new * v_new) + lam_dc * (dcb * dcb - (dcb + de_cand) ** 2)
order = changed[np.argsort(-(d_spec - dsse_cand))]
for k in order:
q_try = int(q_new[b, k])
if q_try == int(out[b, k]):
continue
e_new = xb[k] - (dsb[k] * q_try - msb[k])
de = e_new - eb[k]
dsse = wb[k] * (e_new * e_new - eb[k] * eb[k])
if sseb + dsse > capb:
continue
p = k if k < half else k - half
v_np = vb[p] + de
dc_n = dcb + de
spec_gain = lam_vw * (vb[p] * vb[p] - v_np * v_np) + lam_dc * (dcb * dcb - dc_n * dc_n)
if spec_gain > dsse + 1.0e-18:
out[b, k] = q_try
eb[k] = e_new
vb[p] = v_np
dcb = dc_n
sseb += dsse
return out.astype(np.uint8)
def _refine_trajectory_algebraic(
X: np.ndarray,
X_f64: np.ndarray,
W_f64: np.ndarray,
Xb: np.ndarray,
Wb: np.ndarray,
Sw: np.ndarray,
Sx: np.ndarray,
safe_Sw: np.ndarray,
d0: np.ndarray,
m0: np.ndarray,
ls0: np.ndarray,
lm0: np.ndarray,
codes0: np.ndarray,
carry: np.ndarray,
cfg: HPCConfig,
):
N = X.shape[0]
best_d, best_m = d0.copy(), m0.copy()
best_ls, best_lm = ls0.copy(), lm0.copy()
best_codes = codes0.copy()
best_obj, best_sse = _batch_objective(X, W_f64, best_d, best_m, best_ls, best_lm, best_codes, carry, cfg)
d, m, ls, lm, codes = best_d.copy(), best_m.copy(), best_ls.copy(), best_lm.copy(), best_codes.copy()
ls_deltas = np.array([-3, -2, -1, 0, 1, 2, 3], dtype=np.int64)
lm_deltas = np.array([-1, 0, 1], dtype=np.int64)
n_idx = np.arange(N)[:, None]
s_idx = np.arange(NS)[None, :]
for _ in range(cfg.refine_passes):
d_new, m_new = _batch_extended_dm_solve_and_ulp(X_f64, W_f64, ls, lm, codes, d, m, carry, cfg)
obj_dm, sse_dm = _batch_objective(X, W_f64, d_new, m_new, ls, lm, codes, carry, cfg)
imp_dm = sse_dm < best_sse - 1.0e-15
if np.any(imp_dm):
best_obj = np.where(imp_dm, obj_dm, best_obj)
best_sse = np.where(imp_dm, sse_dm, best_sse)
best_d = np.where(imp_dm, d_new, best_d)
best_m = np.where(imp_dm, m_new, best_m)
best_ls = np.where(imp_dm[:, None], ls, best_ls)
best_lm = np.where(imp_dm[:, None], lm, best_lm)
best_codes = np.where(imp_dm[:, None], codes, best_codes)
d, m = best_d.copy(), best_m.copy()
codes_re = codes_from_params_batch(X, d, m, ls, lm)
obj_re, sse_re = _batch_objective(X, W_f64, d, m, ls, lm, codes_re, carry, cfg)
imp_re = sse_re < best_sse - 1.0e-15
if np.any(imp_re):
best_obj = np.where(imp_re, obj_re, best_obj)
best_sse = np.where(imp_re, sse_re, best_sse)
best_codes = np.where(imp_re[:, None], codes_re, best_codes)
codes = best_codes.copy()
q = codes.astype(np.float64).reshape(N, NS, 16)
Sqq = np.sum(Wb * q * q, axis=2)
Swq = np.sum(Wb * q, axis=2)
Sqx = np.sum(Wb * q * Xb, axis=2)
q_bar = Swq / safe_Sw
Vqq = np.maximum(Sqq - Swq * q_bar, 0.0)
Cqx = Sqx - Sx * q_bar
s_star = np.where(Vqq > 1.0e-30, np.maximum(Cqx / np.maximum(Vqq, 1.0e-30), 0.0), d[:, None] * ls)
sd = np.where(d[:, None] > 1.0e-15, d[:, None], 1.0)
sm = np.where(m[:, None] > 1.0e-15, m[:, None], 1.0)
ls_center = np.where(d[:, None] > 1.0e-15, np.clip(nearest_int((s_star / sd).astype(np.float32)), 0, 15), 0)
c_ls_base = np.clip(ls_center[:, :, None] + ls_deltas[None, None, :], 0, 15).astype(np.float64)
d_sub_base = d.astype(np.float64)[:, None, None] * c_ls_base
t_opt_base = np.maximum(0.0, (d_sub_base * Swq[:, :, None] - Sx[:, :, None]) / safe_Sw[:, :, None])
lm_center = np.where(
m[:, None, None] > 1.0e-15,
np.clip(nearest_int((t_opt_base / sm[:, :, None]).astype(np.float32)), 0, 15),
0,
)
cand_ls = np.repeat(c_ls_base, 3, axis=2)
cand_lm = np.clip(
np.repeat(lm_center, 3, axis=2) + np.tile(lm_deltas, 7)[None, None, :], 0, 15
).astype(np.float64)
d_sub_c = d.astype(np.float64)[:, None, None] * cand_ls
m_sub_c = m.astype(np.float64)[:, None, None] * cand_lm
safe_dc = d_sub_c >= 1.0e-15
sd_c = np.where(safe_dc, d_sub_c, 1.0)
q_c = np.where(
safe_dc[:, :, :, None],
np.clip(
nearest_int(((Xb[:, :, None, :] + m_sub_c[:, :, :, None]) / sd_c[:, :, :, None]).astype(np.float32)),
0,
3,
),
0,
).astype(np.float64)
e_c = Xb[:, :, None, :] - (d_sub_c[:, :, :, None] * q_c - m_sub_c[:, :, :, None])
err_c = np.sum(Wb[:, :, None, :] * e_c * e_c, axis=3)
pick = np.argmin(err_c, axis=2)
ls_try = cand_ls[n_idx, s_idx, pick].astype(np.uint8)
lm_try = cand_lm[n_idx, s_idx, pick].astype(np.uint8)
codes_try = codes_from_params_batch(X, d, m, ls_try, lm_try)
d_try, m_try = _batch_extended_dm_solve_and_ulp(X_f64, W_f64, ls_try, lm_try, codes_try, d, m, carry, cfg)
obj_try, sse_try = _batch_objective(X, W_f64, d_try, m_try, ls_try, lm_try, codes_try, carry, cfg)
imp_sub = sse_try < best_sse - 1.0e-15
if np.any(imp_sub):
best_obj = np.where(imp_sub, obj_try, best_obj)
best_sse = np.where(imp_sub, sse_try, best_sse)
best_d = np.where(imp_sub, d_try, best_d)
best_m = np.where(imp_sub, m_try, best_m)
best_ls = np.where(imp_sub[:, None], ls_try, best_ls)
best_lm = np.where(imp_sub[:, None], lm_try, best_lm)
best_codes = np.where(imp_sub[:, None], codes_try, best_codes)
d, m, ls, lm, codes = best_d.copy(), best_m.copy(), best_ls.copy(), best_lm.copy(), best_codes.copy()
refined = _batch_algebraic_refine_codes(X_f64, W_f64, best_d, best_m, best_ls, best_lm, best_codes, carry, cfg)
d_r, m_r = _batch_extended_dm_solve_and_ulp(X_f64, W_f64, best_ls, best_lm, refined, best_d, best_m, carry, cfg)
obj_r, sse_r = _batch_objective(X, W_f64, d_r, m_r, best_ls, best_lm, refined, carry, cfg)
imp_r = (sse_r <= best_sse * (1.0 + cfg.sse_budget)) & (obj_r < best_obj - 1.0e-15)
if np.any(imp_r):
best_obj = np.where(imp_r, obj_r, best_obj)
best_sse = np.where(imp_r, sse_r, best_sse)
best_d = np.where(imp_r, d_r, best_d)
best_m = np.where(imp_r, m_r, best_m)
best_codes = np.where(imp_r[:, None], refined, best_codes)
return best_d, best_m, best_ls, best_lm, best_codes, best_obj, best_sse
def _phase4_algebraic_batch(
X: np.ndarray,
W: np.ndarray | None,
scales_seed: np.ndarray,
mins_seed: np.ndarray,
base_d: np.ndarray,
base_m: np.ndarray,
best: np.ndarray,
carry: np.ndarray,
standalone: list[FactorResult],
cfg: HPCConfig,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, int]:
"""Batched algebraic Phase-4 solver across N blocks (replaces brute-force 16x16 & ULP grids)."""
N = X.shape[0]
X_f64 = X.astype(np.float64)
W_f64 = np.ones_like(X_f64) if W is None else W.astype(np.float64)
Xb = X_f64.reshape(N, NS, 16)
Wb = W_f64.reshape(N, NS, 16)
Sw = np.sum(Wb, axis=2)
Sx = np.sum(Wb * Xb, axis=2)
safe_Sw = np.maximum(Sw, 1.0e-30)
di = best // N_CAND_M
mi = best % N_CAND_M
d_v = round_fp16(base_d * MULTS_D[di])
m_v = round_fp16(base_m * MULTS_M[mi])
sd_v = np.where(d_v[:, None] > 1.0e-15, d_v[:, None], 1.0)
sm_v = np.where(m_v[:, None] > 1.0e-15, m_v[:, None], 1.0)
ls_v = np.where(d_v[:, None] > 1.0e-15, np.clip(nearest_int(scales_seed / sd_v), 0, 15), 0).astype(np.uint8)
lm_v = np.where(m_v[:, None] > 1.0e-15, np.clip(nearest_int(mins_seed / sm_v), 0, 15), 0).astype(np.uint8)
codes_v = codes_from_params_batch(X, d_v, m_v, ls_v, lm_v)
d1, m1, ls1, lm1, c1, _, sse1 = _refine_trajectory_algebraic(
X, X_f64, W_f64, Xb, Wb, Sw, Sx, safe_Sw, d_v, m_v, ls_v, lm_v, codes_v, carry, cfg
)
cand_hits = 0
fac_hits = 0
if cfg.use_factor_floor:
d_f = np.array([f.d for f in standalone], dtype=np.float32)
m_f = np.array([f.dmin for f in standalone], dtype=np.float32)
ls_f = np.stack([f.ls for f in standalone], axis=0)
lm_f = np.stack([f.lm for f in standalone], axis=0)
codes_f = np.stack([f.codes for f in standalone], axis=0)
d2, m2, ls2, lm2, c2, _, sse2 = _refine_trajectory_algebraic(
X, X_f64, W_f64, Xb, Wb, Sw, Sx, safe_Sw, d_f, m_f, ls_f, lm_f, codes_f, carry, cfg
)
take2 = sse2 < sse1 - 1.0e-15
fac_hits = int(np.sum(take2))
d1 = np.where(take2, d2, d1)
m1 = np.where(take2, m2, m1)
ls1 = np.where(take2[:, None], ls2, ls1)
lm1 = np.where(take2[:, None], lm2, lm1)
c1 = np.where(take2[:, None], c2, c1)
return d1, m1, ls1, lm1, c1, cand_hits, fac_hits
def _apply_dc_nudge(X, W, blocks, cfg, row_width):
n = X.shape[0]
blocks_per_row = row_width // QK_K if row_width and row_width % QK_K == 0 else 0
rolling = 0.0
for i in range(n):
if blocks_per_row > 0 and i % blocks_per_row == 0:
rolling = 0.0
block = blocks[i]
carry = np.float32(cfg.dc_decay * rolling)
d = float(block.d_float)
m = float(block.dmin_float)
ls, lm = block.unpack_scales()
codes = unpack_codes(block.qs)
w = None if W is None else W[i]
codes = dc_nudge_codes(X[i], d, m, ls, lm, codes, w, float(carry), cfg)
blocks[i] = Q2KBlock(
scales=pack_scales(ls, lm),
qs=pack_codes(codes),
d=block.d,
dmin=block.dmin,
)
xh = blocks[i].dequantize()
dc_res = float(np.sum(X[i].astype(np.float64) - xh.astype(np.float64)))
rolling = float(carry) + dc_res if cfg.carry_cumulative else dc_res
return blocks
_MP_STATE: dict[str, object] = {}
def _resolve_workers(cfg: HPCConfig, n_blocks: int) -> int:
if cfg.workers == 1 or n_blocks < 4:
return 1
if cfg.workers > 1:
return min(int(cfg.workers), n_blocks)
if n_blocks < 16:
return 1
cpu = os.cpu_count() or 1
return min(cpu, n_blocks)
def _phase2b_one(i: int) -> tuple[np.ndarray, FactorResult]:
X = _MP_STATE["X"]
W = _MP_STATE["W"]
seeds = _MP_STATE["seeds"]
base_d = _MP_STATE["base_d"]
base_m = _MP_STATE["base_m"]
cfg = _MP_STATE["cfg"]
wi = None if W is None else W[i]
cand = factor_candidates(
X[i],
wi,
seeds["scales"][i],
seeds["mins"][i],
base_d[i],
base_m[i],
cfg,
)
fac_res = factor_only_solve(X[i], wi, cfg)
return cand, fac_res
def _phase4_one(args: tuple[int, int, float, FactorResult]) -> tuple[Q2KBlock, int, int, float]:
i, best_i, carry_i, fac = args
X = _MP_STATE["X"]
W = _MP_STATE["W"]
seeds = _MP_STATE["seeds"]
base_d = _MP_STATE["base_d"]
base_m = _MP_STATE["base_m"]
cfg = _MP_STATE["cfg"]
wi = None if W is None else W[i]
d, m, ls, lm, codes, obj, sse = _assemble_block(
X[i], wi, seeds, base_d[i], base_m[i], best_i, carry_i, cfg
)
cand_hit = 0
fac_hit = 0
if cfg.use_candidate_floor:
cd, cm, cls, clm = _candidate_config(
seeds["scales"][i], seeds["mins"][i], base_d[i], base_m[i], best_i
)
ccodes = codes_from_params(X[i], cd, cm, cls, clm)
cobj, csse = objective(
X[i], wi, cd, cm, cls, clm, ccodes, cfg, carry_i
)
if cobj < obj - 1.0e-15:
d, m, ls, lm, codes, obj, sse = cd, cm, cls, clm, ccodes, cobj, csse
cand_hit = 1
if cfg.use_factor_floor:
fcodes = (
refine_codes_hadamard(
X[i], wi, fac.d, fac.dmin, fac.ls, fac.lm, fac.codes, cfg, carry_i
)
if carry_i != 0.0
else fac.codes
)
fobj, fsse = (
objective(X[i], wi, fac.d, fac.dmin, fac.ls, fac.lm, fcodes, cfg, carry_i)
if carry_i != 0.0
else (fac.objective, fac.sse)
)
if fobj < obj - 1.0e-15:
d = float(fac.d)
m = float(fac.dmin)
ls = fac.ls.copy()
lm = fac.lm.copy()
codes = fcodes.copy()
obj, sse = fobj, fsse
fac_hit = 1
blk_obj = make_block(d, m, ls, lm, codes)
return blk_obj, cand_hit, fac_hit, blk_obj.error(X[i])
class Q2KQuantizer:
def __init__(self, config: HPCConfig | None = None):
self.config = (config or HPCConfig()).validate()
def quantize(
self,
weights: np.ndarray,
importance: np.ndarray | None = None,
row_width: int | None = None,
status_callback=None,
stop_event=None,
) -> QuantizedTensor:
def _emit(phase: str, done: int, total: int, detail: str, **extra) -> None:
if status_callback is not None:
status_callback(
{
"phase": phase,
"done": done,
"total": total,
"detail": detail,
**extra,
}
)
def _check_stop() -> None:
if stop_event is not None and stop_event.is_set():
raise InterruptedError("Quantization cancelled by user")
cfg = self.config
x = np.asarray(weights, dtype=np.float32)
if x.size == 0:
raise ValueError("weight tensor is empty")
if x.size % QK_K != 0:
raise ValueError("weight count must be a multiple of 256")
if x.ndim == 2 and row_width is None and x.shape[1] % QK_K == 0:
row_width = int(x.shape[1])
x = x.ravel()
n_blocks = x.size // QK_K
if cfg.device != "cpu":
from .cuda_backend import cuda_is_available, quantize_cuda
if cfg.device.startswith("cuda") or (
cfg.device == "auto" and cfg.workers == 0 and n_blocks >= 32 and cuda_is_available()
):
dev_str = "cuda" if cfg.device == "auto" else cfg.device
res = quantize_cuda(
weights,
importance,
row_width,
cfg,
status_callback=status_callback,
stop_event=stop_event,
device=dev_str,
)
if cfg.verbose:
_report(res.stats)
return res
X = x.reshape(n_blocks, QK_K)
if importance is not None:
imp = np.asarray(importance, dtype=np.float32).ravel()
if imp.size != x.size:
raise ValueError("importance must match the weight count")
if np.any(imp < 0.0) or not np.all(np.isfinite(imp)):
raise ValueError("importance must be finite and non-negative")
W_raw = imp.reshape(n_blocks, QK_K)
if cfg.temper_imatrix:
sig2 = np.mean(X.astype(np.float64) ** 2, axis=1, keepdims=True)
W = (
np.sqrt(np.maximum(W_raw.astype(np.float64), 1.0e-6))
* np.sqrt(sig2 + X.astype(np.float64) ** 2)
)
w_mean = np.mean(W, axis=1, keepdims=True)
W = np.where(w_mean > 1.0e-30, W / np.maximum(w_mean, 1.0e-30), 1.0).astype(np.float32)
else:
W = W_raw
else:
W = None
sigma = float(np.sqrt(np.mean(x.astype(np.float64) ** 2)))
_check_stop()
clamp_val = seed_clamp_value(x, cfg)
_emit(
"Phase 1: Kurtosis Clamp & Batched GGML Seeds",
0,
n_blocks,
f"blocks={n_blocks} sigma={sigma:.5f} clamp={clamp_val:.5f} imatrix={'yes' if W is not None else 'no'}",
sigma=sigma,
)
seeds = build_seeds(X, W, cfg, clamp_val)
_check_stop()
_emit(
"Phase 2a: Vectorized Superblock WLS",
0,
n_blocks,
f"Solving 5-iteration WLS across {n_blocks} superblocks",
sigma=sigma,
)
base_d, base_m = _phase2_wls(X, W, seeds, cfg)
candidate_errors = np.full((n_blocks, TOTAL_CANDIDATES), np.inf, dtype=np.float32)
standalone: list[FactorResult] = []
t_cand = time.time()
last_emit = 0.0
step_report = max(1, min(32, n_blocks // 20 or 1))
n_workers = _resolve_workers(cfg, n_blocks)
_MP_STATE.update(
{
"X": X,
"W": W,
"seeds": seeds,
"base_d": base_d,
"base_m": base_m,
"cfg": cfg,
}
)
blocks: list[Q2KBlock] = []
factor_floor_used = 0
candidate_floor_used = 0
running_sse = 0.0
if n_workers > 1 and "fork" in mp.get_all_start_methods():
ctx = mp.get_context("fork")
chunksize = max(1, min(16, n_blocks // (n_workers * 4) or 1))
pool = ctx.Pool(processes=n_workers)
try:
for i, (cand_row, fac_res) in enumerate(
pool.imap(_phase2b_one, range(n_blocks), chunksize=chunksize)
):
_check_stop()
candidate_errors[i] = cand_row
standalone.append(fac_res)
now = time.time()
if (i + 1) % step_report == 0 or (i + 1) == n_blocks or (now - last_emit) >= 0.25:
last_emit = now
elapsed = max(now - t_cand, 1.0e-6)
rate = (i + 1) / elapsed
eta = (n_blocks - (i + 1)) / max(rate, 1.0e-9)
_emit(
f"Phase 2b: Analog-Guided Factored Field ({n_workers} workers)",
i + 1,
n_blocks,
f"blk {i+1}/{n_blocks} ({rate:.1f} blk/s, ETA {eta:.1f}s) "
f"d={float(fac_res.d):.5f} dmin={float(fac_res.dmin):.5f} "
f"obj={fac_res.objective:.5e} starts={fac_res.starts}",
rate=rate,
eta=eta,
sigma=sigma,
)
pool.close()
pool.join()
except BaseException:
pool.terminate()
pool.join()
raise
else:
for i in range(n_blocks):
_check_stop()
cand_row, fac_res = _phase2b_one(i)
candidate_errors[i] = cand_row
standalone.append(fac_res)
now = time.time()
if (i + 1) % step_report == 0 or (i + 1) == n_blocks or (now - last_emit) >= 0.25:
last_emit = now
elapsed = max(now - t_cand, 1.0e-6)
rate = (i + 1) / elapsed
eta = (n_blocks - (i + 1)) / max(rate, 1.0e-9)
_emit(
"Phase 2b: Analog-Guided Factored Candidate Field",
i + 1,
n_blocks,
f"blk {i+1}/{n_blocks} ({rate:.1f} blk/s, ETA {eta:.1f}s) "
f"d={float(fac_res.d):.5f} dmin={float(fac_res.dmin):.5f} "
f"obj={fac_res.objective:.5e} starts={fac_res.starts}",
rate=rate,
eta=eta,
sigma=sigma,
)
_MP_STATE.clear()
_check_stop()
_emit(
"Phase 3: 6-State Sieve & 36-State Viterbi DP",
0,
n_blocks,
f"Running Sieve + 36-state Viterbi sequence optimization ({cfg.optimizer.upper()})",
sigma=sigma,
)
best, temperature, _ = _phase3_select(candidate_errors, cfg)
carry = _compute_dc_carry(X, seeds, best, base_d, base_m, cfg, row_width)
_emit(
"Phase 3.9: Viterbi Path & Rolling DC Carry",
n_blocks,
n_blocks,
f"Boltzmann temp={temperature:.4e}, max |DC carry|={float(np.max(np.abs(carry))):.4e}",
temperature=temperature,
sigma=sigma,
)
t_asm = time.time()
batch_sz = 256
for start in range(0, n_blocks, batch_sz):
_check_stop()
stop = min(start + batch_sz, n_blocks)
W_chunk = None if W is None else W[start:stop]
d_b, m_b, ls_b, lm_b, codes_b, c_hits, f_hits = _phase4_algebraic_batch(
X[start:stop],
W_chunk,
seeds["scales"][start:stop],
seeds["mins"][start:stop],
base_d[start:stop],
base_m[start:stop],
best[start:stop],
carry[start:stop],
standalone[start:stop],
cfg,
)
candidate_floor_used += c_hits
factor_floor_used += f_hits
for k in range(stop - start):
blk_obj = make_block(d_b[k], m_b[k], ls_b[k], lm_b[k], codes_b[k])
blocks.append(blk_obj)
running_sse += blk_obj.error(X[start + k])
now = time.time()
elapsed = max(now - t_asm, 1.0e-6)
rate = stop / elapsed
eta = (n_blocks - stop) / max(rate, 1.0e-9)
cur_rmse = float(np.sqrt(running_sse / (stop * QK_K)))
_emit(
"Phase 4: Algebraic Coupled Bilinear Assembly",
stop,
n_blocks,
f"blk {stop}/{n_blocks} ({rate:.1f} blk/s, ETA {eta:.1f}s) "
f"rmse={cur_rmse:.6e} rmse/sigma={cur_rmse/max(sigma, 1e-15):.4f} "
f"floors(cand={candidate_floor_used}, fac={factor_floor_used})",
rmse=cur_rmse,
sigma=sigma,
temperature=temperature,
cand_floors=candidate_floor_used,
fac_floors=factor_floor_used,
rate=rate,
eta=eta,
)
therm_improved = 0
if cfg.therm_passes > 0 and n_blocks > 0:
_check_stop()
t_therm = time.time()
_emit(
"Phase 4.5: Thermal Refinery (Pair Heat-Bath)",
0,
n_blocks,
f"annealing {n_blocks} blocks: passes={cfg.therm_passes} "
f"sweeps={cfg.therm_sweeps} T0={cfg.therm_t0} decay={cfg.therm_t_decay} "
f"seed={cfg.therm_seed}",
sigma=sigma,
temperature=temperature,
)
therm_d = np.empty(n_blocks, dtype=np.float64)
therm_m = np.empty(n_blocks, dtype=np.float64)
therm_ls = np.empty((n_blocks, NS), dtype=np.uint8)
therm_lm = np.empty((n_blocks, NS), dtype=np.uint8)
therm_codes = np.empty((n_blocks, QK_K), dtype=np.uint8)
for i, b in enumerate(blocks):
therm_d[i] = b.d_float
therm_m[i] = b.dmin_float
therm_ls[i], therm_lm[i] = b.unpack_scales()
therm_codes[i] = unpack_codes(b.qs)
last_therm = [0.0, 0]
def _therm_progress(done, total, improved):
now = time.time()
if (now - last_therm[0]) >= 0.25 or done == total:
last_therm[0] = now
_emit(
"Phase 4.5: Thermal Refinery (Pair Heat-Bath)",
done,
total,
f"blk {done}/{total} ({max(done, 1)/max(now - t_therm, 1.0e-6):.1f} blk/s) "
f"improved={improved}",
sigma=sigma,
temperature=temperature,
)
therm_d, therm_m, therm_ls, therm_lm, therm_codes, therm_improved = thermal_refine_tensor(
X,
None if W is None else W,
therm_d,
therm_m,
therm_ls,
therm_lm,
therm_codes,
carry,
cfg,
dm_solve=_batch_extended_dm_solve_and_ulp,
progress=_therm_progress,
)
blocks = [
make_block(therm_d[i], therm_m[i], therm_ls[i], therm_lm[i], therm_codes[i])
for i in range(n_blocks)
]
_emit(
"Phase 4.5: Thermal Refinery (Pair Heat-Bath)",
n_blocks,
n_blocks,
f"done in {time.time() - t_therm:.1f}s, blocks improved={therm_improved}/{n_blocks}",
sigma=sigma,
temperature=temperature,
)
if cfg.pin_dc and n_blocks > 0:
_check_stop()
t_pin = time.time()
_emit(
"Phase 4.7: Zero-DC Pin (Q2_K Free Lunch)",
0,
n_blocks,
"re-solving (d, m) on the exact dc=0 constraint line "
"(exact-JS zero-coherent-residual pin, SSE-nondecreasing)",
sigma=sigma,
temperature=temperature,
)
pin_d = np.empty(n_blocks, dtype=np.float64)
pin_m = np.empty(n_blocks, dtype=np.float64)
pin_ls = np.empty((n_blocks, NS), dtype=np.uint8)
pin_lm = np.empty((n_blocks, NS), dtype=np.uint8)
pin_codes = np.empty((n_blocks, QK_K), dtype=np.uint8)
for i, b in enumerate(blocks):
pin_d[i] = b.d_float
pin_m[i] = b.dmin_float
pin_ls[i], pin_lm[i] = b.unpack_scales()
pin_codes[i] = unpack_codes(b.qs)
pin_d, pin_m = pin_q2k_dc_residual(
pin_d, pin_m, pin_ls, pin_lm, pin_codes, X.astype(np.float64)
)
blocks = [
make_block(pin_d[i], pin_m[i], pin_ls[i], pin_lm[i], pin_codes[i])
for i in range(n_blocks)
]
_emit(
"Phase 4.7: Zero-DC Pin (Q2_K Free Lunch)",
n_blocks,
n_blocks,
f"done in {time.time() - t_pin:.1f}s",
sigma=sigma,
temperature=temperature,
)
if cfg.apply_dc_carry and (cfg.dc_lambda > 0.0 or cfg.dc_decay > 0.0):
_check_stop()
_emit(
"Phase 4.8: Sequential Row-Aware DC Residual Nudge",
n_blocks,
n_blocks,
f"Cancelling cumulative row DC residuals (row_width={row_width or QK_K})",
sigma=sigma,
temperature=temperature,
)
blocks = _apply_dc_nudge(X, W, blocks, cfg, row_width)
total_sse = 0.0
weighted = 0.0
for i, b in enumerate(blocks):
e = X[i].astype(np.float64) - b.dequantize().astype(np.float64)
total_sse += float(np.dot(e, e))
if W is None:
weighted += float(np.dot(e, e))
else:
weighted += float(np.dot(W[i].astype(np.float64), e * e))
rmse = float(np.sqrt(total_sse / x.size))
stats = TensorStats(
n_blocks=n_blocks,
total_sse=total_sse,
weighted_sse=weighted,
rmse=rmse,
sigma=sigma,
rmse_over_sigma=rmse / sigma if sigma > 1.0e-15 else 0.0,
temperature=temperature,
factor_floor_used=factor_floor_used,
candidate_floor_used=candidate_floor_used,
therm_improved=therm_improved,
)
_emit(
"Phase 5: Tensor Complete",
n_blocks,
n_blocks,
f"Done: blocks={n_blocks} rmse={rmse:.6e} rmse/sigma={stats.rmse_over_sigma:.5f} "
f"wSSE={weighted:.5e} temp={temperature:.3e} "
f"floors(cand={candidate_floor_used}, fac={factor_floor_used})",
rmse=rmse,
sigma=sigma,
temperature=temperature,
cand_floors=candidate_floor_used,
fac_floors=factor_floor_used,
therm_improved=therm_improved,
)
scales, qs, dm = _stack_blocks(blocks)
factor_objectives = np.array([r.objective for r in standalone], dtype=np.float64)
result = QuantizedTensor(
blocks=blocks,
scales=scales,
qs=qs,
dm=dm,
best_candidate=best,
factor_objectives=factor_objectives,
config=cfg,
stats=stats,
)
if cfg.verbose:
_report(stats)
return result
def _stack_blocks(blocks):
n = len(blocks)
scales = np.empty((n, NS), dtype=np.uint8)
qs = np.empty((n, QK_K // 4), dtype=np.uint8)
dm = np.empty((n, 2), dtype=np.uint16)
for i, b in enumerate(blocks):
scales[i] = b.scales
qs[i] = b.qs
dm[i, 0] = b.d
dm[i, 1] = b.dmin
return scales, qs, dm
def _report(stats: TensorStats) -> None:
extra = f" therm={stats.therm_improved}" if stats.therm_improved else ""
print(
f"Q2_K V2: blocks={stats.n_blocks} rmse={stats.rmse:.6e} "
f"rmse/sigma={stats.rmse_over_sigma:.5f} temp={stats.temperature:.3e} "
f"floors(cand={stats.candidate_floor_used}, factor={stats.factor_floor_used}){extra}"
)
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