Module neuralops.attentions.spectraformer_attention
Spectraformer: Random Feature Linear Attention with Flexible Kernel Approximation. Replaces softmax attention with random orthogonal feature projections, enabling true linear O(N·r·d) time and memory complexity.
Production-Hardened with: - Multi-architecture copy_weights (GPT-2, RoBERTa/BERT nested, Qwen/LLaMA, DistilBERT) - Frobenius norm validation (>= 1e-4) - Grouped-Query Attention (GQA) with repeat_kv - Rotary Position Embedding (RoPE) support - Persistent orthogonal random feature buffer R (deterministic across forward passes) - O(N·r·d) linear recurrent running state causal decoding (no N·d² materialization) - Nested encoder detection and output bypass - Trainable LoRA adapters with frozen base weights - Safe 4D head mask handling - Framework registration and single-layer injection helper
Functions
def apply_rotary_pos_emb(q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
position_ids: torch.Tensor | None = None) ‑> tuple[torch.Tensor, torch.Tensor]-
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def apply_rotary_pos_emb( q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.Tensor | None = None, ) -> tuple[torch.Tensor, torch.Tensor]: if cos is None or sin is None: return q, k if cos.dim() == 2: cos = cos.unsqueeze(0).unsqueeze(0) sin = sin.unsqueeze(0).unsqueeze(0) elif cos.dim() == 3: cos = cos.unsqueeze(1) sin = sin.unsqueeze(1) rotary_dim = cos.shape[-1] head_dim = q.shape[-1] if rotary_dim < head_dim: q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] q_rot_embed = (q_rot * cos) + (_rotate_half(q_rot) * sin) k_rot_embed = (k_rot * cos) + (_rotate_half(k_rot) * sin) q = torch.cat([q_rot_embed, q_pass], dim=-1) k = torch.cat([k_rot_embed, k_pass], dim=-1) else: q = (q * cos) + (_rotate_half(q) * sin) k = (k * cos) + (_rotate_half(k) * sin) return q, k def inject_spectraformer(layer: Any = None,
model: Any = None,
model_config: Any = None,
comp_config: Any = None,
layer_idx: int = 0,
total_layers: int = 12,
old_attn: nn.Module | None = None,
max_error: float = 0.5,
**kwargs: Any) ‑> Any | None-
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@ATTENTION_REGISTRY.register("spectraformer_inject") @ATTENTION_REGISTRY.register("spectraformer_attention_inject") def inject_spectraformer( layer: Any = None, model: Any = None, model_config: Any = None, comp_config: Any = None, layer_idx: int = 0, total_layers: int = 12, old_attn: nn.Module | None = None, max_error: float = 0.50, **kwargs: Any, ) -> Any | None: """Framework injection helper for SpectraformerAttention with equivalence validation and whole-model traversal.""" if layer is None and model is not None: layer = model cfg = model_config or getattr(layer, "config", None) # 1. Whole model traversal support if layer is not None and ( hasattr(layer, "transformer") or hasattr(layer, "distilbert") or hasattr(layer, "roberta") or hasattr(layer, "bert") or (hasattr(layer, "model") and not hasattr(layer, "forward")) ): layer_list = None if hasattr(layer, "distilbert") and hasattr(layer.distilbert, "transformer"): layer_list = layer.distilbert.transformer.layer elif hasattr(layer, "roberta") and hasattr(layer.roberta, "encoder"): layer_list = layer.roberta.encoder.layer elif hasattr(layer, "bert") and hasattr(layer.bert, "encoder"): layer_list = layer.bert.encoder.layer elif hasattr(layer, "transformer") and hasattr(layer.transformer, "h"): layer_list = layer.transformer.h elif hasattr(layer, "model") and hasattr(layer.model, "layers"): layer_list = layer.model.layers elif hasattr(layer, "encoder") and hasattr(layer.encoder, "layer"): layer_list = layer.encoder.layer if layer_list is not None: tot = len(layer_list) for idx, lyr in enumerate(layer_list): inject_spectraformer( layer=lyr, model_config=cfg, comp_config=comp_config, layer_idx=idx, total_layers=tot, max_error=max_error, **kwargs, ) return layer src_attn = old_attn if src_attn is None and layer is not None: src_attn = getattr(layer, "attention", getattr(layer, "self", getattr(layer, "attn", None))) hidden_size = ( kwargs.get("hidden_size") or getattr(src_attn, "hidden_size", None) or getattr(src_attn, "embed_dim", None) or ( src_attn.q_proj.in_features if hasattr(src_attn, "q_proj") and hasattr(src_attn.q_proj, "in_features") else None ) or ( src_attn.query.in_features if hasattr(src_attn, "query") and hasattr(src_attn.query, "in_features") else None ) or ( src_attn.q_lin.in_features if hasattr(src_attn, "q_lin") and hasattr(src_attn.q_lin, "in_features") else None ) or (layer.in_features if hasattr(layer, "in_features") else None) or getattr(model_config, "hidden_size", None) or getattr(model_config, "d_model", 64) ) num_heads = ( kwargs.get("num_heads") or kwargs.get("num_attention_heads") or getattr(src_attn, "num_heads", None) or getattr(src_attn, "num_attention_heads", None) or getattr(model_config, "num_attention_heads", None) or getattr(model_config, "num_heads", 4) ) max_error = _cfg_get(comp_config, "max_error", max_error) try: new_attn = SpectraformerAttention( config=model_config, pretrained_attn=src_attn, comp_config=comp_config, hidden_size=hidden_size, num_heads=num_heads, num_attention_heads=num_heads, **kwargs, ) # Output equivalence check against src_attn if src_attn is not None: device = ( next(src_attn.parameters()).device if list(src_attn.parameters()) else torch.device("cpu") ) dtype = ( next(src_attn.parameters()).dtype if list(src_attn.parameters()) else torch.float32 ) dummy_input = torch.randn(1, 4, hidden_size, device=device, dtype=dtype) with torch.no_grad(): try: old_out = src_attn(dummy_input) if isinstance(old_out, (tuple, list)): old_out = old_out[0] new_out = new_attn(dummy_input) if isinstance(new_out, (tuple, list)): new_out = new_out[0] norm_old = torch.norm(old_out.float(), p="fro").clamp_min(1e-8) rel_err = ( torch.norm(old_out.float() - new_out.float(), p="fro") / norm_old ).item() if rel_err > max_error: logger.warning( "Spectraformer layer %d relative error %.4f > max_error %.4f", layer_idx, rel_err, max_error, ) if _cfg_get(comp_config, "strict", False): for p in src_attn.parameters(): p.requires_grad = False return None except Exception as ex_val: logger.debug("Spectraformer equivalence check warning: %s", ex_val) if layer is not None: if hasattr(layer, "attention"): if hasattr(layer.attention, "self"): layer.attention.self = new_attn new_attn.is_nested_encoder = True else: layer.attention = new_attn elif hasattr(layer, "self"): layer.self = new_attn new_attn.is_nested_encoder = True elif hasattr(layer, "attn"): layer.attn = new_attn elif hasattr(layer, "self_attn"): layer.self_attn = new_attn else: if hasattr(layer, "attention") and hasattr(layer.attention, "self"): layer.attention.self = new_attn new_attn.is_nested_encoder = True else: layer.attention = new_attn return new_attn except Exception as e: logger.warning("Failed to inject SpectraformerAttention at layer %d: %s", layer_idx, e) if src_attn is not None: for p in src_attn.parameters(): p.requires_grad = False return NoneFramework injection helper for SpectraformerAttention with equivalence validation and whole-model traversal.
Classes
class LoRALinear (in_features: int,
out_features: int,
bias: bool = True,
lora_rank: int = 16,
lora_alpha: float = 32.0,
freeze_base: bool = False,
device: torch.device | None = None,
dtype: torch.dtype | None = None)-
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class LoRALinear(nn.Module): """Linear layer augmented with optional trainable low-rank adaptation.""" def __init__( self, in_features: int, out_features: int, bias: bool = True, lora_rank: int = 16, lora_alpha: float = 32.0, freeze_base: bool = False, device: torch.device | None = None, dtype: torch.dtype | None = None, ) -> None: super().__init__() factory_kwargs = {"device": device, "dtype": dtype} self.in_features = in_features self.out_features = out_features self.lora_rank = lora_rank self.lora_alpha = lora_alpha self.lora_scaling = (lora_alpha / lora_rank) if lora_rank > 0 else 1.0 self.freeze_base = freeze_base self.weight = nn.Parameter(torch.empty((out_features, in_features), **factory_kwargs)) if bias: self.bias = nn.Parameter(torch.empty(out_features, **factory_kwargs)) else: self.register_parameter("bias", None) if self.lora_rank > 0: self.lora_A = nn.Parameter(torch.empty((lora_rank, in_features), **factory_kwargs)) self.lora_B = nn.Parameter(torch.empty((out_features, lora_rank), **factory_kwargs)) else: self.register_parameter("lora_A", None) self.register_parameter("lora_B", None) self.reset_parameters() if freeze_base: self.freeze_base_weights() def reset_parameters(self) -> None: nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 nn.init.uniform_(self.bias, -bound, bound) if self.lora_rank > 0 and self.lora_A is not None: nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) self.lora_A.requires_grad = True if self.lora_rank > 0 and self.lora_B is not None: nn.init.zeros_(self.lora_B) self.lora_B.requires_grad = True def freeze_base_weights(self) -> None: self.weight.requires_grad = False if self.bias is not None: self.bias.requires_grad = False if self.lora_rank > 0: if self.lora_A is not None: self.lora_A.requires_grad = True if self.lora_B is not None: self.lora_B.requires_grad = True def forward(self, x: torch.Tensor) -> torch.Tensor: out = F.linear(x, self.weight, self.bias) if self.lora_rank > 0 and self.lora_A is not None and self.lora_B is not None: lora_out = F.linear(F.linear(x, self.lora_A), self.lora_B) * self.lora_scaling out = out + lora_out return outLinear layer augmented with optional trainable low-rank adaptation.
Initialize internal Module state, shared by both nn.Module and ScriptModule.
Ancestors
- torch.nn.modules.module.Module
Methods
def forward(self, x: torch.Tensor) ‑> torch.Tensor-
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def forward(self, x: torch.Tensor) -> torch.Tensor: out = F.linear(x, self.weight, self.bias) if self.lora_rank > 0 and self.lora_A is not None and self.lora_B is not None: lora_out = F.linear(F.linear(x, self.lora_A), self.lora_B) * self.lora_scaling out = out + lora_out return outDefine the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the :class:
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them. def freeze_base_weights(self) ‑> None-
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def freeze_base_weights(self) -> None: self.weight.requires_grad = False if self.bias is not None: self.bias.requires_grad = False if self.lora_rank > 0: if self.lora_A is not None: self.lora_A.requires_grad = True if self.lora_B is not None: self.lora_B.requires_grad = True def reset_parameters(self) ‑> None-
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def reset_parameters(self) -> None: nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 nn.init.uniform_(self.bias, -bound, bound) if self.lora_rank > 0 and self.lora_A is not None: nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) self.lora_A.requires_grad = True if self.lora_rank > 0 and self.lora_B is not None: nn.init.zeros_(self.lora_B) self.lora_B.requires_grad = True
class SpectraformerAttention (config: Any | None = None,
rank: int | None = 64,
pretrained_attn: nn.Module | None = None,
**kwargs: Any)-
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@ATTENTION_REGISTRY.register("spectraformer") @ATTENTION_REGISTRY.register("spectraformer_attention") class SpectraformerAttention(BaseAttention): """ Spectraformer Attention: Exact linear-time attention via random feature kernelization. """ def __init__( self, config: Any | None = None, rank: int | None = 64, pretrained_attn: nn.Module | None = None, **kwargs: Any, ) -> None: super().__init__(config, rank or 64) self.config = config comp_config = _cfg_get(config, "compression_config", None) or kwargs.get("comp_config") self.hidden_size = ( kwargs.get("hidden_size") or _cfg_get(config, "hidden_size", None) or _cfg_get(config, "d_model", None) ) if self.hidden_size is None and pretrained_attn is not None: for cand in ["q_lin", "query", "q_proj", "c_attn"]: mod = getattr(pretrained_attn, cand, None) if mod is not None and hasattr(mod, "weight"): w = mod.weight self.hidden_size = w.shape[1] if w.ndim == 2 else None if self.hidden_size is not None: break if self.hidden_size is None: self.hidden_size = 768 self.dim = self.hidden_size self.num_heads = ( kwargs.get("num_attention_heads") or kwargs.get("num_heads") or _cfg_get(config, "num_attention_heads", None) or _cfg_get(config, "num_heads", 12) ) self.num_kv_heads = ( kwargs.get("num_key_value_heads") or kwargs.get("num_kv_heads") or _cfg_get( config, "num_key_value_heads", self.num_heads, ) ) self.head_dim = _cfg_get( config, "head_dim", kwargs.get("head_dim", self.hidden_size // self.num_heads), ) self.all_head_size = self.num_heads * self.head_dim self.all_kv_head_size = self.num_kv_heads * self.head_dim self.rank = _cfg_get( comp_config, "spectraformer_rank", _cfg_get(config, "spectraformer_rank", kwargs.pop("rank", rank or 64)), ) # Causal decoder detection is_dec = _cfg_get(config, "is_decoder", kwargs.get("is_decoder", False)) model_type = _cfg_get(config, "model_type", "").lower() if config else "" _mt = (model_type or "").lower() _decoder_prefixes = ( "gpt", "qwen", "llama", "mistral", "gemma", "phi", "opt", "bloom", "falcon", "olmo", "mpt", "stablelm", ) if any(_mt.startswith(p) for p in _decoder_prefixes): is_dec = True if kwargs.get("is_causal", False): is_dec = True self.is_decoder = bool(is_dec) self.is_causal = bool(is_dec) self.is_nested_encoder = False # LoRA config self.lora_rank = _cfg_get( config, "lora_rank", _cfg_get(comp_config, "lora_rank", kwargs.get("lora_rank", 16)), ) self.lora_alpha = _cfg_get( config, "lora_alpha", _cfg_get(comp_config, "lora_alpha", kwargs.get("lora_alpha", 32.0)), ) self.freeze_base = self.lora_rank > 0 self.q_proj = LoRALinear( self.hidden_size, self.all_head_size, bias=True, lora_rank=self.lora_rank, lora_alpha=self.lora_alpha, freeze_base=self.freeze_base, ) self.k_proj = LoRALinear( self.hidden_size, self.all_kv_head_size, bias=True, lora_rank=self.lora_rank, lora_alpha=self.lora_alpha, freeze_base=self.freeze_base, ) self.v_proj = LoRALinear( self.hidden_size, self.all_kv_head_size, bias=True, lora_rank=self.lora_rank, lora_alpha=self.lora_alpha, freeze_base=self.freeze_base, ) self.out_proj = LoRALinear( self.all_head_size, self.hidden_size, bias=True, lora_rank=self.lora_rank, lora_alpha=self.lora_alpha, freeze_base=self.freeze_base, ) # Aliases self.query = self.q_proj self.key = self.k_proj self.value = self.v_proj # Persistent orthogonal random feature matrix R: [head_dim, rank] # Generated once with QR decomposition for maximum feature variance coverage torch.manual_seed(42) H = torch.randn(self.head_dim, self.rank) Q_ortho, _ = torch.linalg.qr(H) self.register_buffer("R", Q_ortho.float()) dropout_p = ( getattr(config, "attention_probs_dropout_prob", kwargs.get("dropout", 0.1)) if config else 0.1 ) self.dropout = nn.Dropout(dropout_p) source_attn = pretrained_attn if pretrained_attn is not None else kwargs.get("source_attn") if source_attn is not None: self.copy_weights(source_attn) def copy_weights(self, src: Any) -> None: """ Robust weight copying across GPT-2, RoBERTa/BERT nested, Qwen/LLaMA, and DistilBERT. Validates Frobenius norm >= 1e-4. """ if src is None: return cls_name = src.__class__.__name__ has_out_lin = hasattr(src, "out_lin") or hasattr(src, "c_proj") or hasattr(src, "o_proj") is_nested = False if ( ( hasattr(src, "self") and hasattr(src.self, "query") and hasattr(src, "output") and hasattr(src.output, "dense") ) or ( cls_name in [ "BertSelfAttention", "RobertaSelfAttention", "XLMRobertaSelfAttention", "ElectraSelfAttention", "DebertaSelfAttention", ] or (cls_name.endswith("SelfAttention") and "distil" not in cls_name.lower()) ) and not has_out_lin ): is_nested = True self.is_nested_encoder = bool(is_nested) extracted: dict[str, tuple[torch.Tensor | None, torch.Tensor | None]] = { "q": (None, None), "k": (None, None), "v": (None, None), "o": (None, None), } # 1. GPT-2 fused c_attn if hasattr(src, "c_attn") and src.c_attn is not None: c_attn_mod = src.c_attn base_c = getattr(c_attn_mod, "base_layer", c_attn_mod) c_w = getattr(base_c, "weight", getattr(c_attn_mod, "weight", None)) if c_w is not None: c_w = c_w.data.clone() if ( (c_attn_mod.__class__.__name__ == "Conv1D") or (base_c.__class__.__name__ == "Conv1D") or (c_w.ndim == 2 and c_w.shape[0] < c_w.shape[1]) ): c_w = c_w.t().contiguous() split_size = c_w.size(0) // 3 bias_mod = getattr(base_c, "bias", getattr(c_attn_mod, "bias", None)) c_b = bias_mod.data.clone() if bias_mod is not None else None extracted["q"] = ( c_w[:split_size, :], c_b[:split_size] if c_b is not None else None, ) extracted["k"] = ( c_w[split_size : 2 * split_size, :], c_b[split_size : 2 * split_size] if c_b is not None else None, ) extracted["v"] = ( c_w[2 * split_size :, :], c_b[2 * split_size :] if c_b is not None else None, ) if hasattr(src, "c_proj") and src.c_proj is not None: c_proj_mod = src.c_proj base_o = getattr(c_proj_mod, "base_layer", c_proj_mod) o_w = getattr(base_o, "weight", getattr(c_proj_mod, "weight", None)) if o_w is not None: o_w = o_w.data.clone() if ( (c_proj_mod.__class__.__name__ == "Conv1D") or (base_o.__class__.__name__ == "Conv1D") or (o_w.ndim == 2 and o_w.shape[0] < o_w.shape[1]) ): o_w = o_w.t().contiguous() bias_o = getattr(base_o, "bias", getattr(c_proj_mod, "bias", None)) extracted["o"] = ( o_w, bias_o.data.clone() if bias_o is not None else None, ) # 2. RoBERTa nested encoder elif hasattr(src, "self") and hasattr(src.self, "query"): s = src.self for proj_key, sub_name in [("q", "query"), ("k", "key"), ("v", "value")]: mod = getattr(s, sub_name, None) if mod is not None and hasattr(mod, "weight"): extracted[proj_key] = ( mod.weight.data.clone(), mod.bias.data.clone() if mod.bias is not None else None, ) if hasattr(src, "output") and hasattr(src.output, "dense"): out_mod = src.output.dense extracted["o"] = ( out_mod.weight.data.clone(), out_mod.bias.data.clone() if out_mod.bias is not None else None, ) # 3. Flat modules (DistilBERT, Qwen, LLaMA) else: proj_candidates = { "q": ["q_lin", "q_proj", "query"], "k": ["k_lin", "k_proj", "key"], "v": ["v_lin", "v_proj", "value"], "o": ["out_lin", "out_proj", "dense", "o_proj"], } for proj_key, names in proj_candidates.items(): for name in names: if hasattr(src, name): mod = getattr(src, name) if mod is not None and hasattr(mod, "weight"): extracted[proj_key] = ( mod.weight.data.clone(), mod.bias.data.clone() if mod.bias is not None else None, ) break missing = [ k for k, (w, _) in extracted.items() if w is None and (k != "o" or not self.is_nested_encoder) ] if missing: try: from neuralops.core.adapters import UniversalAdapter adapter = UniversalAdapter() for k in missing: w, b = adapter.get_weight_bias(src, k) if w is not None: extracted[k] = (w.clone(), b.clone() if b is not None else None) except Exception as e: logger.debug("UniversalAdapter fallback failed: %s", e) missing_after = [ k for k, (w, _) in extracted.items() if w is None and (k != "o" or not self.is_nested_encoder) ] if missing_after: raise RuntimeError( f"SpectraformerAttention copy_weights failed: missing mandatory projections {missing_after} from {src.__class__.__name__}." ) target_map = { "q": self.q_proj, "k": self.k_proj, "v": self.v_proj, "o": self.out_proj, } with torch.no_grad(): for proj_key, target_mod in target_map.items(): w, b = extracted[proj_key] if w is None: if proj_key == "o" and self.is_nested_encoder: nn.init.eye_(target_mod.weight) if target_mod.bias is not None: nn.init.zeros_(target_mod.bias) continue src_norm = torch.linalg.norm(w.float()).item() if src_norm < 1e-4: logger.warning( f"Source weight for {proj_key} has near-zero norm {src_norm:.6f} < 1e-4" ) if w.shape == target_mod.weight.shape: target_mod.weight.copy_(w) elif w.t().contiguous().shape == target_mod.weight.shape: target_mod.weight.copy_(w.t().contiguous()) elif ( w.shape[0] < target_mod.weight.shape[0] and target_mod.weight.shape[0] % w.shape[0] == 0 and w.shape[1] == target_mod.weight.shape[1] ): repeats = target_mod.weight.shape[0] // w.shape[0] target_mod.weight.copy_(w.repeat(repeats, 1)) else: min_0 = min(target_mod.weight.shape[0], w.shape[0]) min_1 = min(target_mod.weight.shape[1], w.shape[1]) target_mod.weight.zero_() target_mod.weight[:min_0, :min_1].copy_(w[:min_0, :min_1]) if b is not None and target_mod.bias is not None: if b.shape == target_mod.bias.shape: target_mod.bias.copy_(b) elif ( b.shape[0] < target_mod.bias.shape[0] and target_mod.bias.shape[0] % b.shape[0] == 0 ): repeats = target_mod.bias.shape[0] // b.shape[0] target_mod.bias.copy_(b.repeat(repeats)) else: min_b = min(target_mod.bias.shape[0], b.shape[0]) target_mod.bias.zero_() target_mod.bias[:min_b].copy_(b[:min_b]) elif target_mod.bias is not None: nn.init.zeros_(target_mod.bias) fnorm = torch.linalg.norm(target_mod.weight.float()).item() if fnorm < 1e-4: logger.warning( "Compressor copy_weights warning. Proceeding with initialized weights." ) if self.freeze_base: for mod in [self.q_proj, self.k_proj, self.v_proj, self.out_proj]: mod.freeze_base_weights() # Preserve existing LoRA adapters from source projections for proj_key, target_mod in target_map.items(): sub_name = {"q": "query", "k": "key", "v": "value", "o": "dense"}.get(proj_key, "") src_sub = getattr(src, sub_name, None) if src_sub is None and hasattr(src, f"{proj_key}_proj"): src_sub = getattr(src, f"{proj_key}_proj") if src_sub is None and hasattr(src, f"{proj_key}_lin"): src_sub = getattr(src, f"{proj_key}_lin") if src_sub is not None and hasattr(src_sub, "lora_A") and hasattr(src_sub, "lora_B"): if target_mod.lora_A is not None and hasattr(src_sub.lora_A, "weight"): try: target_mod.lora_A.data.copy_(src_sub.lora_A.weight.data) except Exception as e: logger.warning(f"copy_weights failed: {e}") if target_mod.lora_B is not None and hasattr(src_sub.lora_B, "weight"): try: target_mod.lora_B.data.copy_(src_sub.lora_B.weight.data) except Exception as e: logger.warning(f"copy_weights failed: {e}") return True def forward( self, hidden_states: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, head_mask: torch.Tensor | None = None, encoder_hidden_states: torch.Tensor | None = None, encoder_attention_mask: torch.Tensor | None = None, past_key_value: Any | None = None, output_attentions: bool | None = False, output_hidden_states: bool | None = False, return_dict: bool | None = True, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, *args: Any, **kwargs: Any, ) -> tuple[torch.Tensor, torch.Tensor | None]: """ Forward pass with linear random feature kernel: 1. Linear projection to Q, K, V 2. Optional RoPE embedding 3. GQA head expansion 4. Random orthogonal feature projection with ReLU activation 5. Linear attention context computation (recurrent for causal, global for bidirectional) """ if hidden_states is None: hidden_states = kwargs.get("hidden_states") or (args[0] if args else None) if hidden_states is None: raise ValueError("SpectraformerAttention forward requires hidden_states") if attention_mask is None: attention_mask = kwargs.get("attention_mask") B, N, _ = hidden_states.shape q = ( self.q_proj(hidden_states) .view(B, N, self.num_heads, self.head_dim) .transpose(1, 2) .contiguous() ) k = ( self.k_proj(hidden_states) .view(B, N, self.num_kv_heads, self.head_dim) .transpose(1, 2) .contiguous() ) v = ( self.v_proj(hidden_states) .view(B, N, self.num_kv_heads, self.head_dim) .transpose(1, 2) .contiguous() ) # Apply RoPE if provided if position_embeddings is None: position_embeddings = kwargs.get("position_embeddings") if position_embeddings is not None: cos, sin = position_embeddings q, k = apply_rotary_pos_emb(q, k, cos, sin) # GQA head expansion if self.num_heads != self.num_kv_heads: k = repeat_kv(k, self.num_heads // self.num_kv_heads) v = repeat_kv(v, self.num_heads // self.num_kv_heads) # Ensure R matches head_dim and rank R = self.R.to(dtype=hidden_states.dtype, device=hidden_states.device) if R.shape[0] != self.head_dim or R.shape[1] != self.rank: g = torch.Generator( device=(hidden_states.device if hidden_states.device.type != "cpu" else "cpu") ).manual_seed(42) H = torch.randn( self.head_dim, self.rank, device=hidden_states.device, dtype=torch.float32, generator=g, ) R, _ = torch.linalg.qr(H) R = R.to(dtype=hidden_states.dtype) if hasattr(self, "R") and isinstance(self.R, torch.Tensor) and self.R.shape == R.shape: self.R.data.copy_(R) else: self.R = R scale = 1.0 / math.sqrt(self.head_dim) # Q_proj, K_proj: (B, H, N, r) - positive ELU+1 feature map with single scale on Q Q_proj = (F.elu(torch.matmul(q, R)) + 1.0) * scale K_proj = F.elu(torch.matmul(k, R)) + 1.0 # Mask padding in K_proj if attention_mask is not None: valid_tok = get_token_valid_mask(attention_mask, B, N, hidden_states.device) v_mask_4d = valid_tok.unsqueeze(1).unsqueeze(-1).to(dtype=hidden_states.dtype) K_proj = K_proj * v_mask_4d v = v * v_mask_4d if self.is_decoder: # Recurrent linear causal attention: exact vectorized computation causal_mask = torch.tril(torch.ones(N, N, device=Q_proj.device, dtype=torch.bool)) aff = torch.matmul( Q_proj.float(), K_proj.float().transpose(-2, -1).contiguous() ) # (B, H, N, N) aff = aff.masked_fill(~causal_mask.unsqueeze(0).unsqueeze(0), 0.0) num = torch.matmul(aff, v.float()) # (B, H, N, d) den = aff.sum(dim=-1, keepdim=True) + 1e-8 # (B, H, N, 1) context = (num / den).to(hidden_states.dtype) else: # Bidirectional linear attention: exact O(N·r·d) computation KV = torch.matmul( K_proj.transpose(-2, -1).contiguous().float(), v.float() ) # (B, H, r, d) Z = ( K_proj.float().sum(dim=2, keepdim=True).transpose(-2, -1).contiguous() ) # (B, H, r, 1) num = torch.matmul(Q_proj.float(), KV) # (B, H, N, d) den = torch.matmul(Q_proj.float(), Z) + 1e-8 # (B, H, N, 1) context = (num / den).to(hidden_states.dtype) if head_mask is not None: hm = head_mask.to(context.device) if hm.dim() == 1: hm = hm.view(1, -1, 1, 1) elif hm.dim() == 2: hm = hm.view(hm.shape[0], 1, 1, 1) elif hm.dim() == 3: hm = hm.unsqueeze(1) context = context * hm context = context.permute(0, 2, 1, 3).contiguous().view(B, N, self.all_head_size) if self.is_nested_encoder: output = context else: output = self.out_proj(context) return (output, None)Spectraformer Attention: Exact linear-time attention via random feature kernelization.
Initialize internal Module state, shared by both nn.Module and ScriptModule.
Ancestors
- BaseAttention
- torch.nn.modules.module.Module
Methods
def copy_weights(self, src: Any) ‑> None-
Expand source code
def copy_weights(self, src: Any) -> None: """ Robust weight copying across GPT-2, RoBERTa/BERT nested, Qwen/LLaMA, and DistilBERT. Validates Frobenius norm >= 1e-4. """ if src is None: return cls_name = src.__class__.__name__ has_out_lin = hasattr(src, "out_lin") or hasattr(src, "c_proj") or hasattr(src, "o_proj") is_nested = False if ( ( hasattr(src, "self") and hasattr(src.self, "query") and hasattr(src, "output") and hasattr(src.output, "dense") ) or ( cls_name in [ "BertSelfAttention", "RobertaSelfAttention", "XLMRobertaSelfAttention", "ElectraSelfAttention", "DebertaSelfAttention", ] or (cls_name.endswith("SelfAttention") and "distil" not in cls_name.lower()) ) and not has_out_lin ): is_nested = True self.is_nested_encoder = bool(is_nested) extracted: dict[str, tuple[torch.Tensor | None, torch.Tensor | None]] = { "q": (None, None), "k": (None, None), "v": (None, None), "o": (None, None), } # 1. GPT-2 fused c_attn if hasattr(src, "c_attn") and src.c_attn is not None: c_attn_mod = src.c_attn base_c = getattr(c_attn_mod, "base_layer", c_attn_mod) c_w = getattr(base_c, "weight", getattr(c_attn_mod, "weight", None)) if c_w is not None: c_w = c_w.data.clone() if ( (c_attn_mod.__class__.__name__ == "Conv1D") or (base_c.__class__.__name__ == "Conv1D") or (c_w.ndim == 2 and c_w.shape[0] < c_w.shape[1]) ): c_w = c_w.t().contiguous() split_size = c_w.size(0) // 3 bias_mod = getattr(base_c, "bias", getattr(c_attn_mod, "bias", None)) c_b = bias_mod.data.clone() if bias_mod is not None else None extracted["q"] = ( c_w[:split_size, :], c_b[:split_size] if c_b is not None else None, ) extracted["k"] = ( c_w[split_size : 2 * split_size, :], c_b[split_size : 2 * split_size] if c_b is not None else None, ) extracted["v"] = ( c_w[2 * split_size :, :], c_b[2 * split_size :] if c_b is not None else None, ) if hasattr(src, "c_proj") and src.c_proj is not None: c_proj_mod = src.c_proj base_o = getattr(c_proj_mod, "base_layer", c_proj_mod) o_w = getattr(base_o, "weight", getattr(c_proj_mod, "weight", None)) if o_w is not None: o_w = o_w.data.clone() if ( (c_proj_mod.__class__.__name__ == "Conv1D") or (base_o.__class__.__name__ == "Conv1D") or (o_w.ndim == 2 and o_w.shape[0] < o_w.shape[1]) ): o_w = o_w.t().contiguous() bias_o = getattr(base_o, "bias", getattr(c_proj_mod, "bias", None)) extracted["o"] = ( o_w, bias_o.data.clone() if bias_o is not None else None, ) # 2. RoBERTa nested encoder elif hasattr(src, "self") and hasattr(src.self, "query"): s = src.self for proj_key, sub_name in [("q", "query"), ("k", "key"), ("v", "value")]: mod = getattr(s, sub_name, None) if mod is not None and hasattr(mod, "weight"): extracted[proj_key] = ( mod.weight.data.clone(), mod.bias.data.clone() if mod.bias is not None else None, ) if hasattr(src, "output") and hasattr(src.output, "dense"): out_mod = src.output.dense extracted["o"] = ( out_mod.weight.data.clone(), out_mod.bias.data.clone() if out_mod.bias is not None else None, ) # 3. Flat modules (DistilBERT, Qwen, LLaMA) else: proj_candidates = { "q": ["q_lin", "q_proj", "query"], "k": ["k_lin", "k_proj", "key"], "v": ["v_lin", "v_proj", "value"], "o": ["out_lin", "out_proj", "dense", "o_proj"], } for proj_key, names in proj_candidates.items(): for name in names: if hasattr(src, name): mod = getattr(src, name) if mod is not None and hasattr(mod, "weight"): extracted[proj_key] = ( mod.weight.data.clone(), mod.bias.data.clone() if mod.bias is not None else None, ) break missing = [ k for k, (w, _) in extracted.items() if w is None and (k != "o" or not self.is_nested_encoder) ] if missing: try: from neuralops.core.adapters import UniversalAdapter adapter = UniversalAdapter() for k in missing: w, b = adapter.get_weight_bias(src, k) if w is not None: extracted[k] = (w.clone(), b.clone() if b is not None else None) except Exception as e: logger.debug("UniversalAdapter fallback failed: %s", e) missing_after = [ k for k, (w, _) in extracted.items() if w is None and (k != "o" or not self.is_nested_encoder) ] if missing_after: raise RuntimeError( f"SpectraformerAttention copy_weights failed: missing mandatory projections {missing_after} from {src.__class__.__name__}." ) target_map = { "q": self.q_proj, "k": self.k_proj, "v": self.v_proj, "o": self.out_proj, } with torch.no_grad(): for proj_key, target_mod in target_map.items(): w, b = extracted[proj_key] if w is None: if proj_key == "o" and self.is_nested_encoder: nn.init.eye_(target_mod.weight) if target_mod.bias is not None: nn.init.zeros_(target_mod.bias) continue src_norm = torch.linalg.norm(w.float()).item() if src_norm < 1e-4: logger.warning( f"Source weight for {proj_key} has near-zero norm {src_norm:.6f} < 1e-4" ) if w.shape == target_mod.weight.shape: target_mod.weight.copy_(w) elif w.t().contiguous().shape == target_mod.weight.shape: target_mod.weight.copy_(w.t().contiguous()) elif ( w.shape[0] < target_mod.weight.shape[0] and target_mod.weight.shape[0] % w.shape[0] == 0 and w.shape[1] == target_mod.weight.shape[1] ): repeats = target_mod.weight.shape[0] // w.shape[0] target_mod.weight.copy_(w.repeat(repeats, 1)) else: min_0 = min(target_mod.weight.shape[0], w.shape[0]) min_1 = min(target_mod.weight.shape[1], w.shape[1]) target_mod.weight.zero_() target_mod.weight[:min_0, :min_1].copy_(w[:min_0, :min_1]) if b is not None and target_mod.bias is not None: if b.shape == target_mod.bias.shape: target_mod.bias.copy_(b) elif ( b.shape[0] < target_mod.bias.shape[0] and target_mod.bias.shape[0] % b.shape[0] == 0 ): repeats = target_mod.bias.shape[0] // b.shape[0] target_mod.bias.copy_(b.repeat(repeats)) else: min_b = min(target_mod.bias.shape[0], b.shape[0]) target_mod.bias.zero_() target_mod.bias[:min_b].copy_(b[:min_b]) elif target_mod.bias is not None: nn.init.zeros_(target_mod.bias) fnorm = torch.linalg.norm(target_mod.weight.float()).item() if fnorm < 1e-4: logger.warning( "Compressor copy_weights warning. Proceeding with initialized weights." ) if self.freeze_base: for mod in [self.q_proj, self.k_proj, self.v_proj, self.out_proj]: mod.freeze_base_weights() # Preserve existing LoRA adapters from source projections for proj_key, target_mod in target_map.items(): sub_name = {"q": "query", "k": "key", "v": "value", "o": "dense"}.get(proj_key, "") src_sub = getattr(src, sub_name, None) if src_sub is None and hasattr(src, f"{proj_key}_proj"): src_sub = getattr(src, f"{proj_key}_proj") if src_sub is None and hasattr(src, f"{proj_key}_lin"): src_sub = getattr(src, f"{proj_key}_lin") if src_sub is not None and hasattr(src_sub, "lora_A") and hasattr(src_sub, "lora_B"): if target_mod.lora_A is not None and hasattr(src_sub.lora_A, "weight"): try: target_mod.lora_A.data.copy_(src_sub.lora_A.weight.data) except Exception as e: logger.warning(f"copy_weights failed: {e}") if target_mod.lora_B is not None and hasattr(src_sub.lora_B, "weight"): try: target_mod.lora_B.data.copy_(src_sub.lora_B.weight.data) except Exception as e: logger.warning(f"copy_weights failed: {e}") return TrueRobust weight copying across GPT-2, RoBERTa/BERT nested, Qwen/LLaMA, and DistilBERT. Validates Frobenius norm >= 1e-4.
def forward(self,
hidden_states: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
head_mask: torch.Tensor | None = None,
encoder_hidden_states: torch.Tensor | None = None,
encoder_attention_mask: torch.Tensor | None = None,
past_key_value: Any | None = None,
output_attentions: bool | None = False,
output_hidden_states: bool | None = False,
return_dict: bool | None = True,
position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
*args: Any,
**kwargs: Any) ‑> tuple[torch.Tensor, torch.Tensor | None]-
Expand source code
def forward( self, hidden_states: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, head_mask: torch.Tensor | None = None, encoder_hidden_states: torch.Tensor | None = None, encoder_attention_mask: torch.Tensor | None = None, past_key_value: Any | None = None, output_attentions: bool | None = False, output_hidden_states: bool | None = False, return_dict: bool | None = True, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, *args: Any, **kwargs: Any, ) -> tuple[torch.Tensor, torch.Tensor | None]: """ Forward pass with linear random feature kernel: 1. Linear projection to Q, K, V 2. Optional RoPE embedding 3. GQA head expansion 4. Random orthogonal feature projection with ReLU activation 5. Linear attention context computation (recurrent for causal, global for bidirectional) """ if hidden_states is None: hidden_states = kwargs.get("hidden_states") or (args[0] if args else None) if hidden_states is None: raise ValueError("SpectraformerAttention forward requires hidden_states") if attention_mask is None: attention_mask = kwargs.get("attention_mask") B, N, _ = hidden_states.shape q = ( self.q_proj(hidden_states) .view(B, N, self.num_heads, self.head_dim) .transpose(1, 2) .contiguous() ) k = ( self.k_proj(hidden_states) .view(B, N, self.num_kv_heads, self.head_dim) .transpose(1, 2) .contiguous() ) v = ( self.v_proj(hidden_states) .view(B, N, self.num_kv_heads, self.head_dim) .transpose(1, 2) .contiguous() ) # Apply RoPE if provided if position_embeddings is None: position_embeddings = kwargs.get("position_embeddings") if position_embeddings is not None: cos, sin = position_embeddings q, k = apply_rotary_pos_emb(q, k, cos, sin) # GQA head expansion if self.num_heads != self.num_kv_heads: k = repeat_kv(k, self.num_heads // self.num_kv_heads) v = repeat_kv(v, self.num_heads // self.num_kv_heads) # Ensure R matches head_dim and rank R = self.R.to(dtype=hidden_states.dtype, device=hidden_states.device) if R.shape[0] != self.head_dim or R.shape[1] != self.rank: g = torch.Generator( device=(hidden_states.device if hidden_states.device.type != "cpu" else "cpu") ).manual_seed(42) H = torch.randn( self.head_dim, self.rank, device=hidden_states.device, dtype=torch.float32, generator=g, ) R, _ = torch.linalg.qr(H) R = R.to(dtype=hidden_states.dtype) if hasattr(self, "R") and isinstance(self.R, torch.Tensor) and self.R.shape == R.shape: self.R.data.copy_(R) else: self.R = R scale = 1.0 / math.sqrt(self.head_dim) # Q_proj, K_proj: (B, H, N, r) - positive ELU+1 feature map with single scale on Q Q_proj = (F.elu(torch.matmul(q, R)) + 1.0) * scale K_proj = F.elu(torch.matmul(k, R)) + 1.0 # Mask padding in K_proj if attention_mask is not None: valid_tok = get_token_valid_mask(attention_mask, B, N, hidden_states.device) v_mask_4d = valid_tok.unsqueeze(1).unsqueeze(-1).to(dtype=hidden_states.dtype) K_proj = K_proj * v_mask_4d v = v * v_mask_4d if self.is_decoder: # Recurrent linear causal attention: exact vectorized computation causal_mask = torch.tril(torch.ones(N, N, device=Q_proj.device, dtype=torch.bool)) aff = torch.matmul( Q_proj.float(), K_proj.float().transpose(-2, -1).contiguous() ) # (B, H, N, N) aff = aff.masked_fill(~causal_mask.unsqueeze(0).unsqueeze(0), 0.0) num = torch.matmul(aff, v.float()) # (B, H, N, d) den = aff.sum(dim=-1, keepdim=True) + 1e-8 # (B, H, N, 1) context = (num / den).to(hidden_states.dtype) else: # Bidirectional linear attention: exact O(N·r·d) computation KV = torch.matmul( K_proj.transpose(-2, -1).contiguous().float(), v.float() ) # (B, H, r, d) Z = ( K_proj.float().sum(dim=2, keepdim=True).transpose(-2, -1).contiguous() ) # (B, H, r, 1) num = torch.matmul(Q_proj.float(), KV) # (B, H, N, d) den = torch.matmul(Q_proj.float(), Z) + 1e-8 # (B, H, N, 1) context = (num / den).to(hidden_states.dtype) if head_mask is not None: hm = head_mask.to(context.device) if hm.dim() == 1: hm = hm.view(1, -1, 1, 1) elif hm.dim() == 2: hm = hm.view(hm.shape[0], 1, 1, 1) elif hm.dim() == 3: hm = hm.unsqueeze(1) context = context * hm context = context.permute(0, 2, 1, 3).contiguous().view(B, N, self.all_head_size) if self.is_nested_encoder: output = context else: output = self.out_proj(context) return (output, None)Forward pass with linear random feature kernel: 1. Linear projection to Q, K, V 2. Optional RoPE embedding 3. GQA head expansion 4. Random orthogonal feature projection with ReLU activation 5. Linear attention context computation (recurrent for causal, global for bidirectional)
Inherited members