diff --git a/cli/lora-quant-fidelity.py b/cli/lora-quant-fidelity.py index 8aba5675c..28036ed66 100644 --- a/cli/lora-quant-fidelity.py +++ b/cli/lora-quant-fidelity.py @@ -21,11 +21,15 @@ are measured as they would actually apply: Reported fidelity is per-module ``applied_rho`` (the measured figure for whichever path the loader would take), summarized as a median and an energy-weighted mean over the file's modules; ``requant_rho`` always carries -the if-merged figure. +the if-merged figure. ``snr`` sets the delta against the base weight's own +quantization error (uniform rounding from the grid step, or measured with +``--reference``), in the calibrated norm with ``--calib``: it does not depend +on the apply path and compares checkpoints of different widths. -Works offline against a pre-quantized SDNQ repo (stored tensors + config) or -a bf16 repo with simulated quantization settings, so a combination can be -assessed before committing to a quantized checkpoint. +Works offline against a pre-quantized SDNQ repo (stored tensors + config, +streamed one module at a time so the repo need not fit in memory) or a bf16 +repo with simulated quantization settings, so a combination can be assessed +before committing to a quantized checkpoint. Examples: python cli/lora-quant-fidelity.py --model vladmandic/Krea-2-Base-sdnq-hadamard-uint4 --arch krea2 --lora "~/models/Lora/Krea 2/krea2_turbo_distill_r256.safetensors" @@ -33,9 +37,13 @@ Examples: """ import os +import re import sys import json +import types import argparse +import importlib +import importlib.util sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) os.environ.setdefault('SD_INSTALL_QUIET', '1') @@ -54,6 +62,7 @@ def parse_cli(): parser.add_argument('--json', default=None, help='write full report to this json file') parser.add_argument('--host-rank', type=int, default=256, help='svd hosting cap for non-factorable modules on sub-8-bit formats, mirroring lora_sdnq_host_rank; 0 scores the requantize path instead') parser.add_argument('--calib', default=None, help='activation statistics file (models/calibration/*.safetensors): hosting truncation is then channel-weighted as with lora_sdnq_host_calib, and hosted rho is measured in the activation-weighted norm (the output-error proxy)') + parser.add_argument('--reference', default=None, help='unquantized repo of the same model: the base quantization error each delta competes with is then measured per module instead of estimated from the grid step') parser.add_argument('--fail-under', type=float, default=None, help='exit 2 when median applied fidelity of any lora is below this') return parser.parse_args() @@ -71,6 +80,7 @@ import torch # pylint: disable=wrong-import-position from safetensors import safe_open # pylint: disable=wrong-import-position from rich import print as rprint # pylint: disable=wrong-import-position +from modules import shared # pylint: disable=wrong-import-position,unused-import # shared must initialize before sd_models, which imports back into it from modules.lora import native_adapter, network, network_lora, network_lokr, network_hada, network_oft, network_full, network_ia3, network_glora, network_norm, lora_sdnq # pylint: disable=wrong-import-position from modules.lora.lora_load import NATIVE_DISPATCH # pylint: disable=wrong-import-position from sdnq.quantizer import sdnq_quantize_layer_weight # pylint: disable=wrong-import-position @@ -82,6 +92,7 @@ MODEL_ROOTS = [ os.path.expanduser('~/database/models/Diffusers'), ] device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +ARCH_PACKAGES = {'zimage': 'z_image', 'f2': 'flux', 'minimaxh3': 'minimax'} # arches whose pipelines package is not spelled like the arch # every adapter family the native loader can build, with the module class that owns its # apply-time math. deltas are taken from the production calc_updown so the tool cannot @@ -104,6 +115,8 @@ class StubOnDisk: self.name = os.path.splitext(os.path.basename(path))[0] self.shorthash = '' self.sd_version = 'unknown' + with safe_open(path, framework='pt', device='cpu') as f: + self.metadata = f.metadata() or {} def resolve_model_dir(spec): @@ -129,7 +142,6 @@ def resolve_arch(name): path = NATIVE_DISPATCH.get({'flux2': 'f2', 'ernie': 'ernieimage'}.get(name, name)) if path is None: raise SystemExit(f'unknown arch {name}; choices: {sorted(NATIVE_DISPATCH)} or generic') - import importlib return importlib.import_module(path) @@ -138,21 +150,27 @@ def map_lora_modules(lora_path, arch_mod): Grouping mirrors the native loader: a family is only considered when its marker is present, and groups resolve to model paths through the arch's own - resolver. Fused-split chunks are counted but not analyzed (their apply-time - math is arch-owned). + resolver. A fused save is sliced onto its targets for the lora family; the + other families' chunks are counted but not analyzed (their apply-time math + is arch-owned). """ with safe_open(lora_path, framework='pt', device='cpu') as f: state_dict = {k: f.get_tensor(k) for k in f.keys()} + metadata = f.metadata() or {} prefixes = getattr(arch_mod, 'KNOWN_PREFIXES', native_adapter.KNOWN_PREFIXES_DEFAULT) - bare = getattr(arch_mod, 'BARE_DIFFUSERS_PREFIXES', ()) resolve = getattr(arch_mod, 'resolve_targets', None) or (lambda prefix, base: [(base, None)]) + grouper = getattr(arch_mod, 'group_by_suffixes', None) or native_adapter.group_by_suffixes # an arch that rewrites keys before parsing groups them itself + file_alpha = getattr(arch_mod, 'file_alpha', None) mapped, census, chunked = {}, {}, 0 for fam, _cls, suffixes, markers in FAMILY_SPECS: if not native_adapter.has_marker(state_dict, markers): continue - groups = native_adapter.group_by_suffixes(state_dict, suffixes, prefixes=prefixes, bare_diffusers_prefixes=bare) + groups = grouper(state_dict, suffixes, prefixes=prefixes) if fam == 'lora': groups = {k: w for k, w in groups.items() if 'lora_down.weight' in w and 'lora_up.weight' in w} + alpha = file_alpha(types.SimpleNamespace(filename=lora_path, metadata=metadata)) if file_alpha is not None else None + if alpha is not None and not any('alpha' in w for w in groups.values()): # a file-level alpha applies only to files without alpha tensors, as in try_load_lora + groups = {k: {**w, 'alpha': torch.tensor(float(alpha))} for k, w in groups.items()} else: groups = {k: w for k, w in groups.items() if native_adapter.has_marker({f'x.{s}': None for s in w}, markers)} if not groups: @@ -160,10 +178,18 @@ def map_lora_modules(lora_path, arch_mod): census[fam] = len(groups) for (prefix, base), w in groups.items(): for path, chunk in native_adapter.resolve_group_targets(resolve, prefix, base): - if chunk is not None: - chunked += 1 - continue - mapped.setdefault(path, []).append((fam, w)) # a module can carry several families; the loader applies each + target = w + if chunk is not None: # a fused save spans several modules; only the lora family slices its up factor + target = None + if fam == 'lora': + fused_out = w['lora_up.weight'].shape[0] + target = native_adapter.slice_lora_chunk(w, chunk) + target = native_adapter.slice_dora_scale(target, chunk, fused_out) + target = native_adapter.slice_bias_delta(target, chunk, fused_out) if target is not None else None + if target is None: + chunked += 1 + continue + mapped.setdefault(path, []).append((fam, target)) # a module can carry several families; the loader applies each return mapped, census, chunked @@ -192,17 +218,14 @@ def build_module(fam, path, w, net, sd_module): return cls(net, weights) -def resolve_transformer_cls(arch, class_name): - """Prefer an sdnext-owned transformer class over the upstream diffusers one. - - Arches like krea2 keep checkpoint-style module names in their own class; - the diffusers class of the same name expects diffusers-style keys and - cannot load these state dicts. - """ - if arch and class_name: +def resolve_transformer_cls(arch, class_name, model_dir=None): + """Resolve the transformer class: an sdnext-owned spec class, then diffusers, then a modeling file beside the weights. + Arches like krea2 keep checkpoint-style names in their own class; pruned MiniMax repos ship theirs as remote code.""" + if not class_name: + return None + if arch: try: - import importlib - pkg = importlib.import_module(f'pipelines.{ {"zimage": "z_image", "f2": "flux"}.get(arch, arch) }') + pkg = importlib.import_module(f'pipelines.{ARCH_PACKAGES.get(arch, arch)}') for attr in dir(pkg): if attr.endswith('_SPEC'): cls = getattr(getattr(pkg, attr), 'cls', None) @@ -210,20 +233,107 @@ def resolve_transformer_cls(arch, class_name): return cls except Exception: pass + import diffusers + cls = getattr(diffusers, class_name, None) + if cls is not None or not model_dir: + return cls + for fname in sorted(os.listdir(model_dir)): + path = os.path.join(model_dir, fname) + if not fname.endswith('.py'): + continue + with open(path, encoding='utf-8') as f: + if re.search(rf'^class {re.escape(class_name)}\b', f.read(), re.MULTILINE) is None: + continue + spec = importlib.util.spec_from_file_location(fname[:-3], path) + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + spec.loader.exec_module(module) + return getattr(module, class_name) return None -def load_quantized_model(model_dir, arch=None, class_name=None): - from sdnq.loader import load_sdnq_model - model = load_sdnq_model(model_dir, model_cls=resolve_transformer_cls(arch, class_name), dtype=torch.bfloat16, device='cpu') - layers = {} - for name, module in model.named_modules(): - if getattr(module, 'sdnq_dequantizer', None) is not None: - layers[name] = module - elif module.__class__.__name__ == 'Linear' and getattr(module, 'weight', None) is not None: - layers[name] = module - del model # layer modules own their tensors; the dict keeps them alive - return layers +class QuantRepo: + """Per-module access to a pre-quantized SDNQ repo: a meta skeleton built through the sdnq conversion, each layer's + tensors streamed from the shards while it is analyzed; tensors outside the block stacks stay resident for the arch hooks.""" + + LAYER_KEYS = ('weight', 'bias', 'scale', 'zero_point', 'svd_up', 'svd_down') + + def __init__(self, model_dir, model_config, arch=None): + from accelerate import init_empty_weights + from sdnq import SDNQConfig + from sdnq.quantizer import sdnq_post_load_quant + from sdnq.utils import get_quant_args_from_config + cls = resolve_transformer_cls(arch, model_config.get('_class_name'), model_dir) + if cls is None: + raise SystemExit(f'cannot resolve transformer class {model_config.get("_class_name")} for "{model_dir}"') + quant_config = SDNQConfig.from_dict(model_config['quantization_config']) + with init_empty_weights(): + config = cls.load_config(model_dir) + if hasattr(config, 'pop'): + config.pop('quantization_config', None) + model = cls.from_config(config) + model = sdnq_post_load_quant(model, torch_dtype=torch.bfloat16, pre_quantized=True, **get_quant_args_from_config(quant_config)) + self.model = model + self.model_dir = model_dir + self.handles = {} + index = os.path.join(model_dir, 'diffusion_pytorch_model.safetensors.index.json') + if os.path.isfile(index): + with open(index, encoding='utf-8') as f: + weight_map = json.load(f)['weight_map'] + else: + weight_map = {} + for fname in sorted(os.listdir(model_dir)): + if fname.endswith('.safetensors'): + with safe_open(os.path.join(model_dir, fname), framework='pt', device='cpu') as f: + weight_map.update(dict.fromkeys(f.keys(), fname)) + mapping = getattr(model, '_checkpoint_conversion_mapping', None) or {} + self.shards = {} # model key -> (stored key, shard) + for stored, shard in weight_map.items(): + key = stored + for pattern, replacement in mapping.items(): + key = re.sub(pattern, replacement, key) + self.shards[key] = (stored, shard) + self.layers = {} + for name, module in model.named_modules(): + if getattr(module, 'sdnq_dequantizer', None) is not None or (module.__class__.__name__ == 'Linear' and getattr(module, 'weight', None) is not None): + self.layers[name] = module + stacks = tuple(f'{name}.' for name, module in model.named_modules() if isinstance(module, torch.nn.ModuleList)) + resident = {key: self.get(key) for key in self.shards if not key.startswith(stacks)} + model.load_state_dict(resident, strict=False, assign=True) + + def get(self, key): + entry = self.shards.get(key) + if entry is None: + return None + stored, shard = entry + f = self.handles.get(shard) + if f is None: + f = safe_open(os.path.join(self.model_dir, shard), framework='pt', device='cpu') + self.handles[shard] = f + return f.get_tensor(stored) + + def materialize(self, name): + """Load one layer's stored tensors onto the analysis device and return the layer.""" + from sdnq.quant_utils import prepare_weight_for_matmul, prepare_svd_for_matmul + layer = self.layers[name] + state = {} + for local in self.LAYER_KEYS: + t = self.get(f'{name}.{local}') + if t is not None: + state[local] = t.to(device) + layer.load_state_dict(state, strict=False, assign=True) + deq = getattr(layer, 'sdnq_dequantizer', None) + if deq is not None: # the loader's post-processing, so the dequantizer sees the layout it expects + if deq.use_quantized_matmul and not deq.re_quantize_for_matmul: + layer.weight.data = prepare_weight_for_matmul(layer.weight, matmul_dtype=deq.quantized_matmul_dtype) + if getattr(layer, 'svd_up', None) is not None: + layer.svd_up.data, layer.svd_down.data = prepare_svd_for_matmul(layer.svd_up, layer.svd_down, deq.use_quantized_matmul) + return layer + + def release(self, name): + layer = self.layers[name] + state = {local: torch.empty_like(t, device='meta') for local, t in layer.state_dict().items() if t.device.type != 'meta'} + layer.load_state_dict(state, strict=False, assign=True) class Bf16Repo: @@ -252,7 +362,28 @@ class Bf16Repo: return f.get_tensor(key) -def analyze_module(W_dq, deq_params, mods, calib_rms=None, step_live=None): +def quant_noise_energy(step, shape, hadamard_group, rms): + """Expected energy of the base weight's quantization error, ``step^2/12`` per element under uniform rounding; + with ``rms`` per input channel it is the output-space energy per token.""" + _out, n_in = shape + s = step.to(device, torch.float32) + if s.ndim == 3: + s = s.squeeze(-1) + if s.ndim == 1: + s = s[:, None] + groups = s.shape[1] + glen = n_in // groups + if rms is None: + power = torch.full((groups,), float(glen), device=s.device) + else: + p = rms.to(s.device, torch.float32).square() + if hadamard_group and n_in % hadamard_group == 0: + p = p.view(-1, hadamard_group).mean(1, keepdim=True).expand(-1, hadamard_group).reshape(-1) + power = p.view(groups, glen).sum(1) + return float(((s.square() / 12) @ power).sum()) + + +def analyze_module(W_dq, deq_params, mods, calib_rms=None, step_live=None, noise=None): """Return fidelity metrics for one quantized module and the adapters targeting it. Deltas come from each module's production calc_updown and sum the way the @@ -263,6 +394,14 @@ def analyze_module(W_dq, deq_params, mods, calib_rms=None, step_live=None): With ``step_live`` (the layer's own pre-add scale), the production routing rule applies: a delta fat against the grid whose truncation capture is low reports the requantize path, the way the loader would route it. + + ``snr`` is the delta's energy over the energy of the base weight's own + quantization error, in the calibrated norm when ``calib_rms`` is given: + ``noise`` carries that error's measured ``(plain, weighted)`` energies + against an unquantized reference, else the uniform-rounding estimate from + ``step_live`` stands in. It is independent of the apply path and says + whether what the adapter adds stands above the error the checkpoint + already carries. """ D = None for mod in mods: @@ -274,7 +413,23 @@ def analyze_module(W_dq, deq_params, mods, calib_rms=None, step_live=None): if float(nD) == 0.0: # an all-zero delta (some full-rank extractions carry empty .diff): retention is undefined, not erased return dict(rank=getattr(mods[0], 'dim', None), rms_delta=0.0, rms_weight=float(W_dq.pow(2).mean().sqrt()), step_ratio=None, crossers=None, requant_rho=None, requant_resid=None, - factor_eligible=factor_eligible, hosted=False, applied_rho=None, delta_energy=0.0) + factor_eligible=factor_eligible, hosted=False, applied_rho=None, delta_energy=0.0, + snr=None, snr_plain=None, noise_rms=None, noise_source=None) + snr, snr_plain, noise_rms, noise_source = None, None, None, None + if (noise is not None or step_live is not None) and not deq_params.get('use_codebook', False): + rms = calib_rms.to(device, torch.float32) if (calib_rms is not None and calib_rms.shape[-1] == D.shape[-1]) else None + if noise is not None: + plain, weighted = noise + noise_source = 'measured' + else: + hadamard = deq_params['hadamard_group_size'] if deq_params['use_hadamard'] else 0 + plain = quant_noise_energy(step_live, D.shape, hadamard, None) + weighted = quant_noise_energy(step_live, D.shape, hadamard, rms) if rms is not None else plain + noise_source = 'uniform' + delta_w = float((D * rms).square().sum()) if rms is not None else float(nD.square()) + noise_rms = (plain / D.numel()) ** 0.5 + snr_plain = float(nD.square()) / plain if plain > 0 else None + snr = delta_w / weighted if weighted > 0 else None step_ratio, crossers = None, None if control: W2 = (W_dq + D).to(torch.bfloat16).float() @@ -339,7 +494,7 @@ def analyze_module(W_dq, deq_params, mods, calib_rms=None, step_live=None): return dict(rank=getattr(mods[0], 'dim', None), rms_delta=float(D.pow(2).mean().sqrt()), rms_weight=float(W_dq.pow(2).mean().sqrt()), step_ratio=step_ratio, crossers=crossers, requant_rho=rho, requant_resid=resid, factor_eligible=factor_eligible, hosted=hosted, applied_rho=applied_rho, - delta_energy=float(nD.square())) + delta_energy=float(nD.square()), snr=snr, snr_plain=snr_plain, noise_rms=noise_rms, noise_source=noise_source) def main(): @@ -350,12 +505,17 @@ def main(): model_config = json.load(f) pre_quantized = model_config.get('quantization_config') is not None - quant_layers, bf16_repo = {}, None - quant_stamps, bf16_stamps = {}, {} + repo, bf16_repo, reference = None, None, None + quant_stamps, bf16_stamps, ref_stamps = {}, {}, {} + adapt = getattr(arch_mod, 'adapt_weights', None) # an arch refits deltas onto a live layout that differs from the trained one if pre_quantized: rprint(f'model: "{model_dir}" pre-quantized={pre_quantized}') - quant_layers = load_quantized_model(model_dir, arch=args.arch, class_name=model_config.get('_class_name')) - quant_stamps = stamp_index(quant_layers) + repo = QuantRepo(model_dir, model_config, arch=args.arch) + quant_stamps = stamp_index(repo.layers) + if args.reference: + reference = Bf16Repo(resolve_model_dir(args.reference)) + ref_stamps = stamp_index(k[:-len('.weight')] for k in reference.weight_map if k.endswith('.weight')) + rprint(f'reference: "{reference.model_dir}" tensors={len(reference.weight_map)}') else: bf16_repo = Bf16Repo(model_dir) bf16_stamps = stamp_index(k[:-len('.weight')] for k in bf16_repo.weight_map if k.endswith('.weight')) @@ -370,6 +530,14 @@ def main(): calib_stats = {k: f.get_tensor(k) for k in f.keys()} rprint(f'calib: "{args.calib}" layers={len(calib_stats)}') + noise_energies = {} # per module, shared by every lora: the base error does not depend on the adapter + def measure_noise(lname, err): + rms = calib_stats.get(lname) + plain = float(err.square().sum()) + weighted = float((err * rms.to(device, torch.float32)).square().sum()) if rms is not None and rms.shape[-1] == err.shape[-1] else plain + noise_energies[lname] = (plain, weighted) + return noise_energies[lname] + report = {'model': model_dir, 'pre_quantized': pre_quantized, 'loras': []} worst_effective = 1.0 def write_report(): @@ -389,10 +557,11 @@ def main(): for path in keys: entries = mapped[path] lname = path + noise = None if pre_quantized: - if path not in quant_layers: + if path not in repo.layers: lname = quant_stamps.get(path.replace('.', '_'), '') - layer = quant_layers.get(lname) + layer = repo.layers.get(lname) if layer is None: unmatched.append(path) continue @@ -403,12 +572,23 @@ def main(): if len(deq.original_shape) != 2: non_matrix.append(path) continue + layer = repo.materialize(lname) W_dq = deq(layer.weight, layer.scale, zero_point=layer.zero_point, svd_up=layer.svd_up, svd_down=layer.svd_down, skip_quantized_matmul=deq.use_quantized_matmul, dtype=torch.float32, skip_compile=True).to(device) params = dict(weights_dtype=deq.weights_dtype, group_size=deq.group_size, hadamard_group_size=deq.hadamard_group_size, - use_hadamard=deq.use_hadamard, use_svd=layer.svd_up is not None, svd_rank=deq.svd_rank, svd_steps=deq.svd_steps) + use_hadamard=deq.use_hadamard, use_svd=layer.svd_up is not None, svd_rank=deq.svd_rank, svd_steps=deq.svd_steps, + use_codebook=getattr(deq, 'use_codebook', False)) step_live = layer.scale.detach().to(device) + repo.release(lname) sd_module = layer + if reference is not None: + noise = noise_energies.get(lname) + if noise is None: + W_ref = reference.get(f'{lname}.weight') + if W_ref is None: + W_ref = reference.get(f'{ref_stamps.get(lname.replace(".", "_"), "")}.weight') + if W_ref is not None and tuple(W_ref.shape) == tuple(W_dq.shape): + noise = measure_noise(lname, W_dq - W_ref.to(device, torch.float32)) else: W = bf16_repo.get(f'{path}.weight') if W is None: @@ -430,10 +610,13 @@ def main(): W_dq = deq0(data0['weight'], data0['scale'], zero_point=data0['zero_point'], svd_up=None, svd_down=None, dtype=torch.float32, skip_compile=True) params = dict(weights_dtype=args.dtype, group_size=deq0.group_size, hadamard_group_size=deq0.hadamard_group_size, use_hadamard=deq0.use_hadamard) step_live = data0['scale'].detach() + noise = noise_energies.get(path) or measure_noise(path, W_dq - W.to(device, torch.float32)) # the simulated grid's own error, measured sd_module = make_stub(W.shape) try: + if adapt is not None and pre_quantized: + entries = [(fam, (adapt(sd_module, path, w, transformers=[repo.model]) or w) if fam == 'lora' else w) for fam, w in entries] mods = [build_module(fam, path, w, net, sd_module) for fam, w in entries] - row = analyze_module(W_dq, params, mods, calib_rms=calib_stats.get(lname), step_live=step_live) + row = analyze_module(W_dq, params, mods, calib_rms=calib_stats.get(lname), step_live=step_live, noise=noise) except Exception as e: # a family the tool cannot rebuild must not read as a clean module failed.append(f'{path}: {type(e).__name__}: {e}') del W_dq @@ -452,16 +635,23 @@ def main(): fb_median = sorted(fb)[len(fb) // 2] if fb else None if median_applied is not None: worst_effective = min(worst_effective, median_applied) + snr_rows = [r for r in scored if r['snr'] is not None] + snr_median = sorted(r['snr'] for r in snr_rows)[len(snr_rows) // 2] if snr_rows else None + snr_energy = sum(r['delta_energy'] for r in snr_rows) + snr_weighted = (sum(r['snr'] * r['delta_energy'] for r in snr_rows) / snr_energy) if snr_energy > 0 else None report['loras'].append({'file': lora_path, 'families': census, 'targets': len(mapped), 'unquantized': unquantized, 'unmatched': unmatched, 'non_matrix': non_matrix, 'chunked': chunked, 'failed': failed, 'exact_modules': n_exact, 'fallback_modules': len(fb), 'fallback_median_rho': fb_median, - 'median_applied_rho': median_applied, 'weighted_applied_rho': weighted, 'modules': rows}) + 'median_applied_rho': median_applied, 'weighted_applied_rho': weighted, + 'snr_median': snr_median, 'snr_weighted': snr_weighted, 'modules': rows}) write_report() # rewrite per file so a crash keeps completed work rprint(f'\nlora: "{os.path.basename(lora_path)}" families={census or "none"} targets={len(mapped)} analyzed={len(rows)} scored={len(scored)} exact={n_exact} fallback={len(fb)} unquantized={len(unquantized)} unmatched={len(unmatched)} non_matrix={len(non_matrix)} chunked={chunked} failed={len(failed)}') if median_applied is None: rprint(' no analyzable modules: nothing measured') else: rprint(f' applied fidelity: median={median_applied:.3f} energy-weighted={weighted:.3f}' + (f' (fallback modules land at median rho={fb_median:.3f})' if fb_median is not None else '')) + if snr_median is not None: + rprint(f' delta over base quantization error: snr median={snr_median:.3g} energy-weighted={snr_weighted:.3g} ({snr_rows[0]["noise_source"]} noise, {"calibrated" if calib_stats else "weight-space"} norm)') for f in failed[:3]: rprint(f' [red]could not rebuild[/red]: {f}') if fb: diff --git a/pipelines/minimax/minimax_lora.py b/pipelines/minimax/minimax_lora.py index d6fda7c13..e74a1d740 100644 --- a/pipelines/minimax/minimax_lora.py +++ b/pipelines/minimax/minimax_lora.py @@ -224,12 +224,13 @@ _BIND_KWARGS = dict( ) -def pruned_basis(sd_module, rank, width): - """The AdaLN curve basis of the loaded transformer that owns ``sd_module``, or None on an unpruned model.""" - from modules import shared - pipe = getattr(shared.sd_model, "pipe", shared.sd_model) - for component in ("transformer", "transformer_ref"): - transformer = getattr(pipe, component, None) +def pruned_basis(sd_module, rank, width, transformers=None): + """The AdaLN curve basis of the transformer owning ``sd_module``, or None on an unpruned model.""" + if transformers is None: + from modules import shared + pipe = getattr(shared.sd_model, "pipe", shared.sd_model) + transformers = [getattr(pipe, component, None) for component in ("transformer", "transformer_ref")] + for transformer in transformers: basis = getattr(getattr(transformer, "time_embedder", None), "basis", None) if basis is None or tuple(basis.shape) != (rank, width): continue @@ -238,14 +239,14 @@ def pruned_basis(sd_module, rank, width): return None -def project_pruned_adaln(sd_module, network_key, w): +def project_pruned_adaln(sd_module, network_key, w, transformers=None): """Refit an AdaLN delta trained on the released time embedding onto the pruned curve basis: the pruned class stores ``W @ P``, so ``up @ down`` lands exactly as ``up @ (down @ P)``.""" down = w.get("lora_down.weight") shape = native_adapter.module_shape(sd_module) if down is None or down.ndim != 2 or shape is None or len(shape) != 2 or down.shape[1] == shape[1]: return None - basis = pruned_basis(sd_module, shape[1], down.shape[1]) + basis = pruned_basis(sd_module, shape[1], down.shape[1], transformers) if basis is None: return None projected = dict(w) @@ -254,6 +255,9 @@ def project_pruned_adaln(sd_module, network_key, w): return projected +adapt_weights = project_pruned_adaln # offline tools refit deltas through the same hook the loader binds + + def try_load_lora(name, network_on_disk, lora_scale): return native_adapter.try_load_lora(name, network_on_disk, lora_scale, network_alpha=file_alpha(network_on_disk), adapt_weights=project_pruned_adaln, **_BIND_KWARGS)