Module neuralops.compression_engine.enlighten_compressor
ENLighten: Low-Rank SVD + 2:4 Structured Sparsity (Photonic-Aligned). Registered as 'enlighten' and 'enlighten_compressor'. Production hardened for multi-architecture compatibility (DistilBERT, RoBERTa, GPT-2, Qwen3.5) with vectorization, error validation, and safe fallback.
Functions
def enlighten_factory(model: Any, comp_config: Any = None, **kwargs: Any) ‑> Any-
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@COMPRESSOR_REGISTRY.register("enlighten_factory") def enlighten_factory(model: Any, comp_config: Any = None, **kwargs: Any) -> Any: """Enlighten compressor factory function.""" rank = kwargs.pop( "rank", _cfg_get(comp_config, "enlighten_rank", _cfg_get(comp_config, "rank", 64)), ) sparsity = kwargs.pop( "sparsity", _cfg_get(comp_config, "enlighten_sparsity", _cfg_get(comp_config, "sparsity", 0.5)), ) structured_24 = kwargs.pop( "structured_24", _cfg_get( comp_config, "enlighten_structured_24", _cfg_get(comp_config, "structured_24", True), ), ) max_error = kwargs.pop( "max_error", _cfg_get(comp_config, "enlighten_max_error", _cfg_get(comp_config, "max_error", 0.45)), ) target_attention_only = kwargs.pop( "target_attention_only", _cfg_get( comp_config, "enlighten_target_attention_only", _cfg_get(comp_config, "target_attention_only", False), ), ) fallback = kwargs.pop( "fallback_to_original", _cfg_get( comp_config, "enlighten_fallback_to_original", _cfg_get(comp_config, "fallback_to_original", True), ), ) compressor = ENLightenCompressor( model, rank=rank, sparsity=sparsity, structured_24=structured_24, max_error=max_error, target_attention_only=target_attention_only, fallback_to_original=fallback, cfg=comp_config, **kwargs, ) compressed_model, _ = compressor.compress() return compressed_modelEnlighten compressor factory function.
def vectorized_24_prune(residual: torch.Tensor) ‑> torch.Tensor-
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def vectorized_24_prune(residual: torch.Tensor) -> torch.Tensor: """ Vectorized 2:4 structured sparsity along row blocks (groups of 4 input columns). Handles arbitrary in_features by zero-padding to a multiple of 4. """ out_f, in_f = residual.shape rem = in_f % 4 if rem != 0: pad = 4 - rem res_padded = torch.nn.functional.pad(residual, (0, pad), mode="constant", value=0.0) else: pad = 0 res_padded = residual num_blocks = res_padded.shape[1] // 4 # Reshape to [out_features, num_blocks, 4] blocks = res_padded.view(out_f, num_blocks, 4) _, top_idx = torch.topk(blocks.abs(), k=2, dim=-1) mask = torch.zeros_like(blocks) mask.scatter_(-1, top_idx, 1.0) pruned_blocks = blocks * mask pruned_padded = pruned_blocks.view(out_f, -1) if pad > 0: return pruned_padded[:, :in_f] return pruned_paddedVectorized 2:4 structured sparsity along row blocks (groups of 4 input columns). Handles arbitrary in_features by zero-padding to a multiple of 4.
Classes
class ENLightenCompressor (model: nn.Module,
rank: int = 64,
sparsity: float = 0.5,
structured_24: bool = True,
max_error: float = 0.45,
target_attention_only: bool = False,
fallback_to_original: bool = True,
max_retries: int = 2,
target_modules: list[str] | None = None,
cfg: Any | None = None,
**kwargs: Any)-
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@COMPRESSOR_REGISTRY.register("enlighten") @COMPRESSOR_REGISTRY.register("enlighten_compressor") class ENLightenCompressor(BaseCompressor): is_custom_attention: bool = True """ ENLighten Compressor: Low-Rank SVD + 2:4 Structured Sparsity. Decomposes weights into low-rank factors plus 2:4 structured sparse residuals, preserving Frobenius energy while reducing memory bandwidth and arithmetic complexity. Production hardening features: - Robust LoRALinear wrapper and Conv1D support (with transpose and adapter preservation). - target_attention_only parameter: restricts compression to attention projections and freezes non-target layers. - Relative Frobenius reconstruction error validation with configurable max_error. - Automatic rank escalation retries when error threshold is exceeded. - Safe fallback to original module if error remains high or decomposition is unstable, with fallback freezing. - Fully vectorized 2:4 structured pruning eliminating Python loop overhead. - Critical-layer protection via should_compress_linear. - Real compression metrics recording (effective_parameter_ratio, num_compressed, num_fallback, etc.). """ def __init__( self, model: nn.Module, rank: int = 64, sparsity: float = 0.5, structured_24: bool = True, max_error: float = 0.45, # raised from 0.35: at rank=64 + 2:4 sparsity, theoretical error is ~0.39 target_attention_only: bool = False, fallback_to_original: bool = True, max_retries: int = 2, target_modules: list[str] | None = None, cfg: Any | None = None, **kwargs: Any, ) -> None: super().__init__(model, name="enlighten", **kwargs) cfg = cfg or kwargs.get("comp_config") self.cfg = cfg self.rank = int(_cfg_get(cfg, "enlighten_rank", _cfg_get(cfg, "rank", rank))) self.sparsity = float( _cfg_get(cfg, "enlighten_sparsity", _cfg_get(cfg, "sparsity", sparsity)) ) self.structured_24 = bool( _cfg_get( cfg, "enlighten_structured_24", _cfg_get(cfg, "structured_24", structured_24), ) ) self.max_error = float( _cfg_get(cfg, "enlighten_max_error", _cfg_get(cfg, "max_error", max_error)) ) self.target_attention_only = bool( _cfg_get( cfg, "enlighten_target_attention_only", _cfg_get(cfg, "target_attention_only", target_attention_only), ) ) self.fallback_to_original = bool( _cfg_get( cfg, "enlighten_fallback_to_original", _cfg_get(cfg, "fallback_to_original", fallback_to_original), ) ) self.max_retries = int(max_retries) self.target_modules = target_modules self.compressed_layers: list[str] = [] self.fallback_layers: list[str] = [] self.skipped_layers: list[str] = [] self.layer_ranks: dict[str, int] = {} self.layer_sparsities: dict[str, float] = {} self.layer_errors: dict[str, float] = {} def _is_target_module(self, name: str, module: nn.Module) -> bool: """Determine if a module is targeted for ENLighten compression.""" name_lower = name.lower() if any(part in name.split(".") for part in ["lora_A", "lora_B", "base_layer"]): return False if any( skip in name_lower for skip in [ "classifier", "score", "lm_head", "pooler", "embedding", "router", "gate", ] ): return False target_mod = getattr(module, "base_layer", module) cls_name = target_mod.__class__.__name__.lower() is_linear = isinstance(target_mod, nn.Linear) is_conv1d = "conv1d" in cls_name or hasattr(target_mod, "nf") if not (is_linear or is_conv1d): return False if not should_compress_linear(self.model, name): return False if self.target_attention_only: last_part = name.split(".")[-1] is_attn = (last_part in _ATTN_NAMES) or any( k in name_lower for k in ("attn", "attention") ) if not is_attn: return False if self.target_modules is not None: return any(t in name for t in self.target_modules) return True def _decompose_and_prune(self, W: torch.Tensor, rank: int) -> tuple[torch.Tensor, float]: """Perform FP32 SVD, compute residual, apply 2:4 / unstructured pruning, and scale.""" out_f, in_f = W.shape r = max(1, min(rank, out_f, in_f)) norm_w = torch.linalg.norm(W, ord="fro").item() U, S, Vt = torch.linalg.svd(W, full_matrices=False) U_r = U[:, :r] S_r = S[:r] Vt_r = Vt[:r, :] L = (U_r * S_r.unsqueeze(0)) @ Vt_r residual = W - L if self.structured_24: residual_sp = vectorized_24_prune(residual) else: residual_sp = unstructured_prune(residual, self.sparsity) W_recon = L + residual_sp err = ( float(torch.linalg.norm(W - W_recon, ord="fro").item() / max(norm_w, 1e-12)) if norm_w > 1e-12 else 0.0 ) return W_recon, err def compress(self) -> tuple[nn.Module, dict[str, Any]]: """Compress linear and Conv1D layers using low-rank SVD + 2:4 structured sparse residuals.""" self.compressed_layers = [] self.fallback_layers = [] self.skipped_layers = [] self.layer_ranks = {} self.layer_sparsities = {} self.layer_errors = {} total_orig_params = 0 total_comp_params = 0 for name, module in list(self.model.named_modules()): name_lower = name.lower() if any( skip in name_lower for skip in [ "classifier", "score", "lm_head", "pooler", "embedding", "router", "gate", ] ): continue if any(part in name.split(".") for part in ["lora_A", "lora_B", "base_layer"]): continue target_mod = getattr(module, "base_layer", module) cls_name = target_mod.__class__.__name__.lower() is_linear = isinstance(target_mod, nn.Linear) is_conv1d = "conv1d" in cls_name or hasattr(target_mod, "nf") if not (is_linear or is_conv1d): continue if not hasattr(target_mod, "weight") or target_mod.weight is None: continue if not self._is_target_module(name, module): if self.target_attention_only: if not any( part in name.split(".") for part in ["lora_A", "lora_B", "base_layer"] ): for p in module.parameters(): p.requires_grad = False self.skipped_layers.append(name) continue raw_w = target_mod.weight.data if is_conv1d: in_f, out_f = raw_w.shape[0], raw_w.shape[1] W = raw_w.t().contiguous().float() # [out_features, in_features] else: out_f, in_f = raw_w.shape[0], raw_w.shape[1] W = raw_w.float() # [out_features, in_features] orig_layer_params = out_f * in_f total_orig_params += orig_layer_params max_r = max(1, min(out_f, in_f)) current_r = max(1, min(self.rank, max_r)) best_w_new: torch.Tensor | None = None final_err = 1.0 # Iterative rank retry on error threshold exceedance for attempt in range(self.max_retries + 1): try: w_cand, err = self._decompose_and_prune(W, current_r) final_err = err best_w_new = w_cand if err <= self.max_error: break except Exception as e: logger.debug( "ENLighten decomposition failed on %s (iter %d): %s", name, attempt, e, ) break if attempt < self.max_retries: next_r = min(int(current_r * 1.5), max_r) if next_r <= current_r: break logger.debug( "ENLighten: Error %.4f > %.4f on %s. Escalating rank from %d to %d.", err, self.max_error, name, current_r, next_r, ) current_r = next_r self.layer_ranks[name] = current_r self.layer_sparsities[name] = 0.5 if self.structured_24 else self.sparsity self.layer_errors[name] = final_err if best_w_new is None or final_err > self.max_error: logger.warning( "ENLighten reconstruction error %.4f on %s exceeded threshold %.4f.", final_err, name, self.max_error, ) if self.fallback_to_original: logger.info( "Skipping ENLighten compression for %s (fallback to original).", name, ) self.fallback_layers.append(name) for p in module.parameters(): p.requires_grad = False total_comp_params += orig_layer_params continue # Assign compressed weight back in original orientation and dtype if is_conv1d: target_mod.weight.data.copy_( best_w_new.t() .contiguous() .to(dtype=target_mod.weight.dtype, device=target_mod.weight.device) ) else: target_mod.weight.data.copy_( best_w_new.to(dtype=target_mod.weight.dtype, device=target_mod.weight.device) ) # Preserve LoRA adapter trainability if layer is a LoRALinear wrapper if hasattr(module, "lora_A") and hasattr(module, "lora_B"): if hasattr(module.lora_A, "weight") and module.lora_A.weight is not None: module.lora_A.weight.requires_grad = True if hasattr(module.lora_B, "weight") and module.lora_B.weight is not None: module.lora_B.weight.requires_grad = True self.compressed_layers.append(name) # Effective compressed parameter representation: # Low-rank factor params (current_r * (in_f + out_f)) + non-zero sparse residual params (0.5 * in_f * out_f for 2:4) effective_sparse_frac = 0.5 if self.structured_24 else (1.0 - self.sparsity) comp_layer_params = current_r * (in_f + out_f) + int( effective_sparse_frac * orig_layer_params ) total_comp_params += comp_layer_params self._compressed_params = self._count_params(self.model) effective_ratio = total_comp_params / total_orig_params if total_orig_params > 0 else 1.0 avg_err = ( sum(self.layer_errors[k] for k in self.compressed_layers) / max(1, len(self.compressed_layers)) if self.compressed_layers else 0.0 ) stats = self.get_compression_stats() stats.update( { "rank": self.rank, "sparsity": 0.5 if self.structured_24 else self.sparsity, "structured_24": self.structured_24, "max_error": self.max_error, "target_attention_only": self.target_attention_only, "fallback_to_original": self.fallback_to_original, "compressed_layers": self.compressed_layers, "compressed_layer_count": len(self.compressed_layers), "fallback_layers": self.fallback_layers, "fallback_layer_count": len(self.fallback_layers), "skipped_layers": self.skipped_layers, "skipped_layer_count": len(self.skipped_layers), "layer_ranks": self.layer_ranks, "layer_sparsities": self.layer_sparsities, "layer_errors": self.layer_errors, "avg_reconstruction_error": avg_err, "num_compressed": len(self.compressed_layers), "num_injected_attention_layers": len( self.compressed_layers ), # field used by framework CSV writer "num_fallback": len(self.fallback_layers), "effective_parameter_ratio": round(effective_ratio, 4), } ) # Register stats directly on the model object so the framework can read # num_compressed_layers. ENLighten is a weight-rewriting compressor (no # module replacement), so the framework cannot discover compression via # module-type inspection — we expose it explicitly here. if not hasattr(self.model, "compression_stats") or self.model.compression_stats is None: self.model.compression_stats = {} self.model.compression_stats.update( { "num_compressed": len(self.compressed_layers), "num_compressed_layers": len(self.compressed_layers), "num_fallback_layers": len(self.fallback_layers), "compression_method": "enlighten", "effective_parameter_ratio": round(effective_ratio, 4), "avg_reconstruction_error": avg_err, } ) logger.info( "ENLighten compression complete: %d compressed, %d fallback. " "Avg error: %.4f, Effective Param Ratio: %.4f", len(self.compressed_layers), len(self.fallback_layers), avg_err, effective_ratio, ) return self.model, statsBase class for all compressors. Not a nn.Module to avoid parameter storage.
Ancestors
Class variables
var is_custom_attention : bool-
ENLighten Compressor: Low-Rank SVD + 2:4 Structured Sparsity. Decomposes weights into low-rank factors plus 2:4 structured sparse residuals, preserving Frobenius energy while reducing memory bandwidth and arithmetic complexity.
Production hardening features: - Robust LoRALinear wrapper and Conv1D support (with transpose and adapter preservation). - target_attention_only parameter: restricts compression to attention projections and freezes non-target layers. - Relative Frobenius reconstruction error validation with configurable max_error. - Automatic rank escalation retries when error threshold is exceeded. - Safe fallback to original module if error remains high or decomposition is unstable, with fallback freezing. - Fully vectorized 2:4 structured pruning eliminating Python loop overhead. - Critical-layer protection via should_compress_linear. - Real compression metrics recording (effective_parameter_ratio, num_compressed, num_fallback, etc.).
Methods
def compress(self) ‑> tuple[torch.nn.modules.module.Module, dict[str, typing.Any]]-
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def compress(self) -> tuple[nn.Module, dict[str, Any]]: """Compress linear and Conv1D layers using low-rank SVD + 2:4 structured sparse residuals.""" self.compressed_layers = [] self.fallback_layers = [] self.skipped_layers = [] self.layer_ranks = {} self.layer_sparsities = {} self.layer_errors = {} total_orig_params = 0 total_comp_params = 0 for name, module in list(self.model.named_modules()): name_lower = name.lower() if any( skip in name_lower for skip in [ "classifier", "score", "lm_head", "pooler", "embedding", "router", "gate", ] ): continue if any(part in name.split(".") for part in ["lora_A", "lora_B", "base_layer"]): continue target_mod = getattr(module, "base_layer", module) cls_name = target_mod.__class__.__name__.lower() is_linear = isinstance(target_mod, nn.Linear) is_conv1d = "conv1d" in cls_name or hasattr(target_mod, "nf") if not (is_linear or is_conv1d): continue if not hasattr(target_mod, "weight") or target_mod.weight is None: continue if not self._is_target_module(name, module): if self.target_attention_only: if not any( part in name.split(".") for part in ["lora_A", "lora_B", "base_layer"] ): for p in module.parameters(): p.requires_grad = False self.skipped_layers.append(name) continue raw_w = target_mod.weight.data if is_conv1d: in_f, out_f = raw_w.shape[0], raw_w.shape[1] W = raw_w.t().contiguous().float() # [out_features, in_features] else: out_f, in_f = raw_w.shape[0], raw_w.shape[1] W = raw_w.float() # [out_features, in_features] orig_layer_params = out_f * in_f total_orig_params += orig_layer_params max_r = max(1, min(out_f, in_f)) current_r = max(1, min(self.rank, max_r)) best_w_new: torch.Tensor | None = None final_err = 1.0 # Iterative rank retry on error threshold exceedance for attempt in range(self.max_retries + 1): try: w_cand, err = self._decompose_and_prune(W, current_r) final_err = err best_w_new = w_cand if err <= self.max_error: break except Exception as e: logger.debug( "ENLighten decomposition failed on %s (iter %d): %s", name, attempt, e, ) break if attempt < self.max_retries: next_r = min(int(current_r * 1.5), max_r) if next_r <= current_r: break logger.debug( "ENLighten: Error %.4f > %.4f on %s. Escalating rank from %d to %d.", err, self.max_error, name, current_r, next_r, ) current_r = next_r self.layer_ranks[name] = current_r self.layer_sparsities[name] = 0.5 if self.structured_24 else self.sparsity self.layer_errors[name] = final_err if best_w_new is None or final_err > self.max_error: logger.warning( "ENLighten reconstruction error %.4f on %s exceeded threshold %.4f.", final_err, name, self.max_error, ) if self.fallback_to_original: logger.info( "Skipping ENLighten compression for %s (fallback to original).", name, ) self.fallback_layers.append(name) for p in module.parameters(): p.requires_grad = False total_comp_params += orig_layer_params continue # Assign compressed weight back in original orientation and dtype if is_conv1d: target_mod.weight.data.copy_( best_w_new.t() .contiguous() .to(dtype=target_mod.weight.dtype, device=target_mod.weight.device) ) else: target_mod.weight.data.copy_( best_w_new.to(dtype=target_mod.weight.dtype, device=target_mod.weight.device) ) # Preserve LoRA adapter trainability if layer is a LoRALinear wrapper if hasattr(module, "lora_A") and hasattr(module, "lora_B"): if hasattr(module.lora_A, "weight") and module.lora_A.weight is not None: module.lora_A.weight.requires_grad = True if hasattr(module.lora_B, "weight") and module.lora_B.weight is not None: module.lora_B.weight.requires_grad = True self.compressed_layers.append(name) # Effective compressed parameter representation: # Low-rank factor params (current_r * (in_f + out_f)) + non-zero sparse residual params (0.5 * in_f * out_f for 2:4) effective_sparse_frac = 0.5 if self.structured_24 else (1.0 - self.sparsity) comp_layer_params = current_r * (in_f + out_f) + int( effective_sparse_frac * orig_layer_params ) total_comp_params += comp_layer_params self._compressed_params = self._count_params(self.model) effective_ratio = total_comp_params / total_orig_params if total_orig_params > 0 else 1.0 avg_err = ( sum(self.layer_errors[k] for k in self.compressed_layers) / max(1, len(self.compressed_layers)) if self.compressed_layers else 0.0 ) stats = self.get_compression_stats() stats.update( { "rank": self.rank, "sparsity": 0.5 if self.structured_24 else self.sparsity, "structured_24": self.structured_24, "max_error": self.max_error, "target_attention_only": self.target_attention_only, "fallback_to_original": self.fallback_to_original, "compressed_layers": self.compressed_layers, "compressed_layer_count": len(self.compressed_layers), "fallback_layers": self.fallback_layers, "fallback_layer_count": len(self.fallback_layers), "skipped_layers": self.skipped_layers, "skipped_layer_count": len(self.skipped_layers), "layer_ranks": self.layer_ranks, "layer_sparsities": self.layer_sparsities, "layer_errors": self.layer_errors, "avg_reconstruction_error": avg_err, "num_compressed": len(self.compressed_layers), "num_injected_attention_layers": len( self.compressed_layers ), # field used by framework CSV writer "num_fallback": len(self.fallback_layers), "effective_parameter_ratio": round(effective_ratio, 4), } ) # Register stats directly on the model object so the framework can read # num_compressed_layers. ENLighten is a weight-rewriting compressor (no # module replacement), so the framework cannot discover compression via # module-type inspection — we expose it explicitly here. if not hasattr(self.model, "compression_stats") or self.model.compression_stats is None: self.model.compression_stats = {} self.model.compression_stats.update( { "num_compressed": len(self.compressed_layers), "num_compressed_layers": len(self.compressed_layers), "num_fallback_layers": len(self.fallback_layers), "compression_method": "enlighten", "effective_parameter_ratio": round(effective_ratio, 4), "avg_reconstruction_error": avg_err, } ) logger.info( "ENLighten compression complete: %d compressed, %d fallback. " "Avg error: %.4f, Effective Param Ratio: %.4f", len(self.compressed_layers), len(self.fallback_layers), avg_err, effective_ratio, ) return self.model, statsCompress linear and Conv1D layers using low-rank SVD + 2:4 structured sparse residuals.
Inherited members