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{ "adasvd_compressor": { "module_name": "adasvd_compressor", "source_file": "Codes/framework/compression_engine/adasvd_compressor.py", "source_lines_count": 488, "architecture": { "class_name": "AdaSVDCompressor", "base_class": "BaseCompressor", "init_args": "self, model: nn.Module, rank: Optional[int] = None, target_param_ratio: float = 0.7, num_iters: int = 5, max_error: float = 0.40, fallback_to_original: bool = True, target_attention_only: bool = True, min_rank: int = 16, target_modules: Optional[List[str]] = None, comp_config: Any = None, **kwargs: Any, ) -> None: super().__init__(model, name=\"adasvd\", **kwargs) cfg = comp_config or kwargs.get(\"comp_config\", kwargs.get(\"cfg\", getattr(model, \"config\", None))) self.rank = _cfg_get(cfg, \"adasvd_rank\", _cfg_get(cfg, \"rank\", rank)) self.target_param_ratio = float( _cfg_get(cfg, \"adasvd_target_ratio\", _cfg_get(cfg, \"target_param_ratio\", target_param_ratio)) ) self.num_iters = int(_cfg_get(cfg, \"adasvd_iters\", _cfg_get(cfg, \"num_iters\", num_iters))) self.max_error = float(_cfg_get(cfg, \"adasvd_max_error\", _cfg_get(cfg, \"max_error\", max_error))) self.fallback_to_original = bool( _cfg_get(cfg, \"adasvd_fallback_to_original\", _cfg_get(cfg, \"fallback_to_original\", fallback_to_original)) ) self.target_attention_only = bool( _cfg_get(cfg, \"adasvd_target_attention_only\", _cfg_get(cfg, \"target_attention_only\", target_attention_only)) ) self.min_rank = int(_cfg_get(cfg, \"adasvd_min_rank\", _cfg_get(cfg, \"min_rank\", min_rank))) self.target_modules = target_modules self.comp_config = cfg self.compressed_layers: List[str] = [] self.fallback_layers: List[str] = [] self.layer_errors: Dict[str, float] = {} self.layer_ranks: Dict[str, int] = {} self.layer_iters: Dict[str, int] = {} def _is_target_module(self, name: str, module: nn.Module) -> bool: if isinstance(module, SVDLinear", "registry_names": [ "adasvd" ] }, "params": [], "metrics": { 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"BaseCompressor", "init_args": "self, model: nn.Module, sparsity: float = 0.5, quant_bits: int = 4, rho1: float = 0.1, rho2: float = 0.1, max_iter: int = 8, structured_24: bool = True, block_size: Tuple[int, int] = (4, 4), max_error: float = 0.45, fallback_to_original: bool = True, target_attention_only: bool = False, target_modules: Optional[List[str]] = None, comp_config: Any = None, **kwargs: Any, ) -> None: super().__init__(model, name=\"admm\", **kwargs) cfg = comp_config or kwargs.get(\"comp_config\", kwargs.get(\"cfg\", getattr(model, \"config\", None))) self.sparsity = max(0.0, min(float(_cfg_get(cfg, \"admm_sparsity\", _cfg_get(cfg, \"sparsity\", sparsity))), 0.95)) self.quant_bits = int(_cfg_get(cfg, \"admm_quant_bits\", _cfg_get(cfg, \"quant_bits\", quant_bits))) self.rho1 = float(_cfg_get(cfg, \"admm_rho1\", _cfg_get(cfg, \"rho1\", rho1))) self.rho2 = float(_cfg_get(cfg, \"admm_rho2\", _cfg_get(cfg, \"rho2\", rho2))) self.max_iter = int(_cfg_get(cfg, \"admm_max_iter\", _cfg_get(cfg, \"max_iter\", max_iter))) self.structured_24 = bool(_cfg_get(cfg, \"admm_structured_24\", _cfg_get(cfg, \"structured_24\", structured_24))) self.block_size = block_size self.max_error = float(_cfg_get(cfg, \"admm_max_error\", _cfg_get(cfg, \"max_error\", max_error))) self.fallback_to_original = bool( _cfg_get(cfg, \"admm_fallback_to_original\", _cfg_get(cfg, \"fallback_to_original\", fallback_to_original)) ) self.target_attention_only = bool( _cfg_get(cfg, \"admm_target_attention_only\", _cfg_get(cfg, \"target_attention_only\", target_attention_only)) ) self.target_modules = target_modules self.comp_config = cfg self.compressed_layers: List[str] = [] self.fallback_layers: List[str] = [] self.layer_errors: Dict[str, float] = {} self.layer_sparsities: Dict[str, float] = {} self.layer_actual_sparsities: Dict[str, float] = {} def _is_target_module(self, name: str, module: nn.Module) -> bool: # Do not target inner submodules of LoRA or wrappers directly if any(name.endswith(sfx) for sfx in [\".base_layer\", \".lora_A\", \".lora_B\", \".lora_dropout\", \".linear\"]", "registry_names": [ "admm_compressor", "admm" ] }, "params": [], "metrics": { "results/unified_admm/roberta/roberta-base_full_full_metrics.json": { "model": "roberta-base_full", "performance": { "accuracy_pct": 78.56, "f1_score": 0.8124, "f1_macro": 0.7811, "precision": 0.8051, "recall": 0.7832, "val_loss": 0.4381, "status": "PASS", "elapsed_s": 6.89 }, "efficiency": { "total_params_before": 124647170, "total_params_after": 124647170, "trainable_recovery_params": 0, "compression_rate": 1.0, "compression_ratio_pct": 0.0, "flops_reduction_pct": 0.0, "num_compressed_layers": 0, "num_fallback_layers": 0, "num_injected_attention_layers": 0, "lora_params": 0, "avg_reconstruction_error": null, "weighted_reconstruction_error": null, "per_layer_reconstruction_errors": {}, "per_layer_ranks": {} }, "hardware": { "peak_vram_mb": 712.34, "tokens_per_sec": 23915.43 }, "architecture": { 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"Codes/framework/compression_engine/admm_pruner.py", "source_lines_count": 767, "architecture": { "class_name": "ADMMMaskedLinear", "base_class": "nn.Module", "init_args": "self, in_features: int, out_features: int, bias: bool = True, dtype: torch.dtype = torch.float32, device: Optional[torch.device] = None, ) -> None: super().__init__() self.in_features = in_features self.out_features = out_features self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype, device=device)) self.register_buffer(\"mask\", torch.ones(out_features, in_features, dtype=dtype, device=device)) if bias: self.bias = nn.Parameter(torch.empty(out_features, dtype=dtype, device=device)) else: self.register_parameter(\"bias\", None) @classmethod def from_linear(cls, linear: nn.Linear, mask: torch.Tensor) -> ADMMMaskedLinear: layer = cls( in_features=linear.in_features, out_features=linear.out_features, bias=linear.bias is not None, dtype=linear.weight.dtype, device=linear.weight.device, ) layer.weight.data.copy_(linear.weight.data) layer.mask.copy_(mask.to(dtype=linear.weight.dtype, device=linear.weight.device)) if linear.bias is not None: layer.bias.data.copy_(linear.bias.data) return layer def forward(self, x: torch.Tensor) -> torch.Tensor: w_masked = ADMMSTEPrune.apply(self.weight, self.mask) return F.linear(x, w_masked, self.bias) @COMPRESSOR_REGISTRY.register(\"admm_pruner\") @COMPRESSOR_REGISTRY.register(\"admm_pruner_distill\") class ADMMPruner(BaseCompressor", "registry_names": [ "admm_pruner", "admm_pruner_distill", "admm_pruner_factory" ] }, "params": [ { "name": "weight", "type": "nn.Parameter", "declaration": "nn.Parameter(torch.empty(out_features, in_features, dtype=dtype, device=device)" }, { "name": "bias", "type": "nn.Parameter", "declaration": "nn.Parameter(torch.empty(out_features, dtype=dtype, device=device)" } ], "metrics": { "results/unified_admm_pruner/roberta/roberta-base_full_full_metrics.json": { "model": "roberta-base_full", "performance": { 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"module_name,status,accuracy_pct,f1_score,f1_macro,precision,recall,val_loss,elapsed_s,peak_vram_mb,tokens_per_sec,total_params_before,total_params_after,trainable_recovery_params,compression_rate,compression_ratio_pct,flops_reduction_pct,num_compressed_layers,num_fallback_layers,avg_reconstruction_error,weighted_reconstruction_error,num_injected_attention_layers,lora_params,error,path,timestamp\nfull,PASS,78.56,0.8124,0.7811,0.8051,0.7832,0.4381,6.95,712.34,23592.51,124647170,124647170,0,1.0,0.0,0.0,0,0,,,0,0,,adapted_roberta,2026-09-15T10:07:42.297917\naegis_spectron_fp8,PASS_LOW,49.43,0.3815,0.4769,0.4974,0.4978,0.7801,59.05,572.68,4840.56,124647170,107330390,1520688,1.1613,13.89,0.0,0,0,,,12,1179648,note: evaluated accuracy (49.43%) below chance threshold (56.00%),adapted_roberta,2026-09-15T10:08:41.630327\n", "results/unified_aegis_spectron_fp8/distilbert/summary.csv": 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02:36:48\ntrack2_ssm,aegis_spectron_fp8,attention,t5,mrpc,PASS,,3361.68,1210.73,4572.41,0.0,1.0,4708748,4708748,0.957,1430.7,0.0,0.7033,0.0,0.0,0.0,6.9328,\"{'loss': [0.7033, 0.7033, 0.7033, 0.7033, 0.7033], 'grad_norm': [6.9334, 6.9319, 6.9344, 6.9318, 6.9324], 'fwd_latency_ms': [3341.4114, 3357.3223, 3380.0303, 3371.2205, 3358.427], 'bwd_latency_ms': [1207.1083, 1208.8097, 1210.4514, 1209.3563, 1217.9451]}\",2026-08-26 02:37:16", "results/master_aggregated_results.csv": "aegis_spectron_fp8,0,PASS_LOW,50.92,50.92,50.92,212.0,2026-09-11T10:59:57.016139", "results/master_benchmark_summary.csv": "aegis_spectron_fp8,wikitext2,O(N^2) Softmax,11.25,2.4204,36511,2290.7,14.023,37.972,180.692,0.07,SUCCESS\naegis_spectron_fp8,c4,O(N^2) Softmax,11.25,2.4204,36511,2290.7,14.023,37.972,180.692,0.07,SUCCESS", "results/run_quality_all_screening_summary.csv": 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{
"adasvd_compressor": {
"module_name": "adasvd_compressor",
"source_file": "Codes/framework/compression_engine/adasvd_compressor.py",
"source_lines_count": 488,
"architecture": {
"class_name": "AdaSVDCompressor",
"base_class": "BaseCompressor",
"init_args": "self, model: nn.Module, rank: Optional[int] = None, target_param_ratio: float = 0.7, num_iters: int = 5, max_error: float = 0.40, fallback_to_original: bool = True, target_attention_only: bool = True, min_rank: int = 16, target_modules: Optional[List[str]] = None, comp_config: Any = None, **kwargs: Any, ) -> None: super().__init__(model, name=\"adasvd\", **kwargs) cfg = comp_config or kwargs.get(\"comp_config\", kwargs.get(\"cfg\", getattr(model, \"config\", None))) self.rank = _cfg_get(cfg, \"adasvd_rank\", _cfg_get(cfg, \"rank\", rank)) self.target_param_ratio = float( _cfg_get(cfg, \"adasvd_target_ratio\", _cfg_get(cfg, \"target_param_ratio\", target_param_ratio)) ) self.num_iters = int(_cfg_get(cfg, \"adasvd_iters\", _cfg_get(cfg, \"num_iters\", num_iters))) self.max_error = float(_cfg_get(cfg, \"adasvd_max_error\", _cfg_get(cfg, \"max_error\", max_error))) self.fallback_to_original = bool( _cfg_get(cfg, \"adasvd_fallback_to_original\", _cfg_get(cfg, \"fallback_to_original\", fallback_to_original)) ) self.target_attention_only = bool( _cfg_get(cfg, \"adasvd_target_attention_only\", _cfg_get(cfg, \"target_attention_only\", target_attention_only)) ) self.min_rank = int(_cfg_get(cfg, \"adasvd_min_rank\", _cfg_get(cfg, \"min_rank\", min_rank))) self.target_modules = target_modules self.comp_config = cfg self.compressed_layers: List[str] = [] self.fallback_layers: List[str] = [] self.layer_errors: Dict[str, float] = {} self.layer_ranks: Dict[str, int] = {} self.layer_iters: Dict[str, int] = {} def _is_target_module(self, name: str, module: nn.Module) -> bool: if isinstance(module, SVDLinear",
"registry_names": [
"adasvd"
]
},
"params": [],
"metrics": {
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"model": "roberta-base_full",
"performance": {
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"f1_score": 0.8124,
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"precision": 0.8051,
"recall": 0.7832,
"val_loss": 0.4381,
"status": "PASS",
"elapsed_s": 6.77
},
"efficiency": {
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"trainable_recovery_params": 0,
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"avg_reconstruction_error": null,
"weighted_reconstruction_error": null,
"per_layer_reconstruction_errors": {},
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},
"hardware": {
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"tokens_per_sec": 24228.27
},
"architecture": {
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"mean_span": 128.0,
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},
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"L_1024_vram_mb": "unsupported (exceeds model max_position_embeddings 514)"
},
"timestamp": "2026-09-15T09:56:11.500945"
},
"results/unified_adasvd/roberta/roberta-base_adasvd_full_metrics.json": {
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"performance": {
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"recall": 0.7306,
"val_loss": 0.5381,
"status": "PASS",
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},
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"compression_rate": 1.1359,
"compression_ratio_pct": 11.96,
"flops_reduction_pct": 63.2,
"num_compressed_layers": 40,
"num_fallback_layers": 8,
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"lora_params": 0,
"avg_reconstruction_error": 0.5913941559039471,
"weighted_reconstruction_error": 0.6364,
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