feat: Implement advanced sampling techniques

- Add confidence-guided sampler with dynamic noise scheduling
- Add energy-based sampler with structure validation
- Add attention-based sampler with structure-aware attention
- Add graph-based sampler with message passing
- Add comprehensive test suite for all samplers

This implementation integrates cutting-edge sampling techniques
for improved protein generation, including:
- Dynamic confidence estimation
- Energy-based refinement
- Structure-aware attention routing
- Graph-based message passing
- Local structure preservation

models/sampling/__init__.py

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+"""
+Sampling module for ProteinFlex.
+Implements advanced protein generation sampling techniques.
+"""
+
+from .confidence_guided_sampler import ConfidenceGuidedSampler
+
+__all__ = ['ConfidenceGuidedSampler']

models/sampling/attention_based_sampler.py

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+"""
+Attention-Based Sampling implementation for ProteinFlex.
+Implements structure-aware attention routing for protein generation.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Dict, Tuple, Optional
+
+class StructureAwareAttention(nn.Module):
+    def __init__(
+        self,
+        feature_dim: int,
+        num_heads: int = 8,
+        dropout: float = 0.1
+    ):
+        """Initialize structure-aware attention."""
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = feature_dim // num_heads
+        assert self.head_dim * num_heads == feature_dim, "feature_dim must be divisible by num_heads"
+
+        self.qkv = nn.Linear(feature_dim, 3 * feature_dim)
+        self.structure_proj = nn.Linear(feature_dim, feature_dim)
+        self.output_proj = nn.Linear(feature_dim, feature_dim)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        structure_bias: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """Forward pass with optional structure bias."""
+        B, L, D = x.shape
+        qkv = self.qkv(x).reshape(B, L, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        # Compute attention scores
+        attn = (q @ k.transpose(-2, -1)) * (1.0 / torch.sqrt(torch.tensor(self.head_dim)))
+
+        # Add structure bias if provided
+        if structure_bias is not None:
+            structure_weights = self.structure_proj(structure_bias)
+            structure_weights = structure_weights.view(B, 1, L, L)
+            attn = attn + structure_weights
+
+        attn = F.softmax(attn, dim=-1)
+        attn = self.dropout(attn)
+
+        # Apply attention to values
+        x = (attn @ v).transpose(1, 2).reshape(B, L, D)
+        x = self.output_proj(x)
+
+        return x
+
+class AttentionBasedSampler(nn.Module):
+    def __init__(
+        self,
+        feature_dim: int = 768,
+        hidden_dim: int = 512,
+        num_layers: int = 6,
+        num_heads: int = 8,
+        dropout: float = 0.1
+    ):
+        """
+        Initialize Attention-Based Sampler.
+
+        Args:
+            feature_dim: Dimension of protein features
+            hidden_dim: Hidden dimension for feed-forward
+            num_layers: Number of transformer layers
+            num_heads: Number of attention heads
+            dropout: Dropout rate
+        """
+        super().__init__()
+        self.feature_dim = feature_dim
+        self.hidden_dim = hidden_dim
+
+        # Structure encoder
+        self.structure_encoder = nn.Sequential(
+            nn.Linear(feature_dim, hidden_dim),
+            nn.LayerNorm(hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, feature_dim)
+        )
+
+        # Transformer layers
+        self.layers = nn.ModuleList([
+            nn.ModuleDict({
+                'attention': StructureAwareAttention(feature_dim, num_heads, dropout),
+                'norm1': nn.LayerNorm(feature_dim),
+                'ff': nn.Sequential(
+                    nn.Linear(feature_dim, hidden_dim),
+                    nn.ReLU(),
+                    nn.Dropout(dropout),
+                    nn.Linear(hidden_dim, feature_dim)
+                ),
+                'norm2': nn.LayerNorm(feature_dim)
+            }) for _ in range(num_layers)
+        ])
+
+        # Output projection
+        self.output_proj = nn.Linear(feature_dim, feature_dim)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        structure_info: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """
+        Forward pass for training.
+
+        Args:
+            x: Input protein features [batch_size, seq_len, feature_dim]
+            structure_info: Optional structure information
+
+        Returns:
+            Processed features
+        """
+        # Process structure information if provided
+        structure_bias = None
+        if structure_info is not None:
+            structure_bias = self.structure_encoder(structure_info)
+
+        # Apply transformer layers
+        for layer in self.layers:
+            # Attention with structure bias
+            attn_out = layer['attention'](
+                layer['norm1'](x),
+                structure_bias
+            )
+            x = x + attn_out
+
+            # Feed-forward
+            ff_out = layer['ff'](layer['norm2'](x))
+            x = x + ff_out
+
+        return self.output_proj(x)
+
+    def sample(
+        self,
+        batch_size: int,
+        seq_len: int,
+        device: torch.device,
+        structure_info: Optional[torch.Tensor] = None,
+        temperature: float = 1.0
+    ) -> torch.Tensor:
+        """
+        Generate protein features using attention-based sampling.
+
+        Args:
+            batch_size: Number of samples to generate
+            seq_len: Sequence length
+            device: Device to generate on
+            structure_info: Optional structure information
+            temperature: Sampling temperature
+
+        Returns:
+            Generated protein features
+        """
+        # Initialize from random
+        x = torch.randn(batch_size, seq_len, self.feature_dim, device=device)
+
+        # Apply temperature scaling
+        x = x * temperature
+
+        # Generate features with structure guidance
+        return self.forward(x, structure_info)
+
+    def compute_loss(
+        self,
+        pred_features: torch.Tensor,
+        target_features: torch.Tensor,
+        structure_info: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """
+        Compute training loss.
+
+        Args:
+            pred_features: Predicted protein features
+            target_features: Target protein features
+            structure_info: Optional structure information
+
+        Returns:
+            Loss value
+        """
+        # Feature reconstruction loss
+        recon_loss = F.mse_loss(pred_features, target_features)
+
+        # Structure-aware loss if structure info provided
+        if structure_info is not None:
+            structure_pred = self.structure_encoder(pred_features)
+            structure_loss = F.mse_loss(structure_pred, structure_info)
+            return recon_loss + structure_loss
+
+        return recon_loss

models/sampling/confidence_guided_sampler.py

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+"""
+Confidence-Guided Sampling implementation for ProteinFlex.
+Based on recent advances in protein structure generation.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from typing import Dict, Tuple, Optional
+
+class ConfidenceGuidedSampler(nn.Module):
+    def __init__(
+        self,
+        feature_dim: int = 768,
+        hidden_dim: int = 512,
+        num_steps: int = 1000,
+        min_beta: float = 1e-4,
+        max_beta: float = 0.02
+    ):
+        """
+        Initialize the Confidence-Guided Sampler.
+
+        Args:
+            feature_dim: Dimension of protein features
+            hidden_dim: Hidden dimension for confidence network
+            num_steps: Number of diffusion steps
+            min_beta: Minimum noise schedule value
+            max_beta: Maximum noise schedule value
+        """
+        super().__init__()
+        self.feature_dim = feature_dim
+        self.hidden_dim = hidden_dim
+
+        # Confidence estimation network
+        self.confidence_net = nn.Sequential(
+            nn.Linear(feature_dim, hidden_dim),
+            nn.LayerNorm(hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, hidden_dim // 2),
+            nn.LayerNorm(hidden_dim // 2),
+            nn.ReLU(),
+            nn.Linear(hidden_dim // 2, 1),
+            nn.Sigmoid()
+        )
+
+        # Noise prediction network
+        self.noise_pred_net = nn.Sequential(
+            nn.Linear(feature_dim + hidden_dim, hidden_dim),  # +hidden_dim for time embedding
+            nn.LayerNorm(hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.LayerNorm(hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, feature_dim)
+        )
+
+        # Setup noise schedule
+        self.num_steps = num_steps
+        self.register_buffer('betas', torch.linspace(min_beta, max_beta, num_steps))
+        alphas = 1 - self.betas
+        self.register_buffer('alphas_cumprod', torch.cumprod(alphas, dim=0))
+
+    def get_time_embedding(self, t: torch.Tensor) -> torch.Tensor:
+        """Generate time embeddings."""
+        half_dim = self.hidden_dim // 2
+        embeddings = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        embeddings = torch.exp(torch.arange(half_dim, device=t.device) * -embeddings)
+        embeddings = t[:, None] * embeddings[None, :]
+        embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)
+        return embeddings
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        noise: Optional[torch.Tensor] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Forward pass for training.
+
+        Args:
+            x: Input protein features [batch_size, seq_len, feature_dim]
+            noise: Optional pre-generated noise
+
+        Returns:
+            Tuple of (noisy features, predicted noise)
+        """
+        batch_size = x.shape[0]
+
+        # Generate noise if not provided
+        if noise is None:
+            noise = torch.randn_like(x)
+
+        # Sample timestep
+        t = torch.randint(0, self.num_steps, (batch_size,), device=x.device)
+
+        # Get noise scaling
+        a = self.alphas_cumprod[t]
+        a = a.view(-1, 1, 1)
+
+        # Add noise to input
+        noisy_x = torch.sqrt(a) * x + torch.sqrt(1 - a) * noise
+
+        # Predict noise
+        time_emb = self.get_time_embedding(t)
+        time_emb = time_emb.view(batch_size, 1, -1).expand(-1, x.shape[1], -1)
+        pred_input = torch.cat([noisy_x, time_emb], dim=-1)
+        pred_noise = self.noise_pred_net(pred_input)
+
+        return noisy_x, pred_noise
+
+    def sample(
+        self,
+        batch_size: int,
+        seq_len: int,
+        device: torch.device,
+        temperature: float = 1.0
+    ) -> torch.Tensor:
+        """
+        Generate protein features using confidence-guided sampling.
+
+        Args:
+            batch_size: Number of samples to generate
+            seq_len: Sequence length
+            device: Device to generate on
+            temperature: Sampling temperature
+
+        Returns:
+            Generated protein features
+        """
+        # Start from random noise
+        x = torch.randn(batch_size, seq_len, self.feature_dim, device=device)
+
+        # Iterative refinement
+        for t in reversed(range(self.num_steps)):
+            # Get confidence score
+            confidence = self.confidence_net(x)
+
+            # Predict and remove noise
+            time_emb = self.get_time_embedding(torch.tensor([t], device=device))
+            time_emb = time_emb.expand(batch_size, seq_len, -1)
+            pred_input = torch.cat([x, time_emb], dim=-1)
+            pred_noise = self.noise_pred_net(pred_input)
+
+            # Update features based on confidence
+            alpha = self.alphas_cumprod[t]
+            alpha_prev = self.alphas_cumprod[t-1] if t > 0 else torch.tensor(1.0, device=device)
+            beta = 1 - alpha / alpha_prev
+
+            # Apply confidence-guided update
+            mean = (x - beta * pred_noise) / torch.sqrt(1 - beta)
+            var = beta * temperature * (1 - confidence)
+            x = mean + torch.sqrt(var) * torch.randn_like(x)
+
+        return x
+
+    def compute_loss(
+        self,
+        x: torch.Tensor,
+        noise: torch.Tensor,
+        pred_noise: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Compute training loss.
+
+        Args:
+            x: Input protein features
+            noise: Target noise
+            pred_noise: Predicted noise
+
+        Returns:
+            Combined loss value
+        """
+        # MSE loss for noise prediction
+        noise_loss = F.mse_loss(pred_noise, noise)
+
+        # Confidence loss to encourage accurate confidence estimation
+        confidence = self.confidence_net(x)
+        confidence_target = torch.exp(-F.mse_loss(pred_noise, noise, reduction='none').mean(-1))
+        confidence_loss = F.binary_cross_entropy(confidence, confidence_target)
+
+        return noise_loss + confidence_loss