[FEAT][GeneticDiffusionModule]

alphafold3/diffusion.py

@@ -0,0 +1,94 @@
+import torch
+from torch import nn, Tensor
+import torch.nn.functional as F
+
+
+class GeneticDiffusionModule(nn.Module):
+    """
+    Diffusion Module from AlphaFold 3.
+
+    This module directly predicts raw atom coordinates via a generative diffusion process.
+    It leverages a diffusion model trained to denoise 'noised' atomic coordinates back to their
+    true state. The diffusion process captures both local and global structural information
+    through a series of noise scales.
+
+    Attributes:
+        channels (int): The number of channels in the input feature map, corresponding to atomic features.
+        num_diffusion_steps (int): The number of diffusion steps or noise levels to use.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        num_diffusion_steps: int = 1000,
+        training: bool = False,
+    ):
+        """
+        Initializes the DiffusionModule with the specified number of channels and diffusion steps.
+
+        Args:
+            channels (int): Number of feature channels for the input.
+            num_diffusion_steps (int): Number of diffusion steps (time steps in the diffusion process).
+        """
+        super(GeneticDiffusionModule, self).__init__()
+        self.channels = channels
+        self.num_diffusion_steps = num_diffusion_steps
+        self.training = training
+        self.noise_scale = nn.Parametr(
+            torch.linspace(1.0, 0.01, num_diffusion_steps)
+        )
+        self.prediction_network = nn.Sequential(
+            nn.Linear(channels, channels * 2),
+            nn.ReLU(),
+            nn.Linear(channels * 2, channels),
+        )
+
+    def forward(self, x: Tensor = None, ground_truth: Tensor = None):
+        """
+        Forward pass of the DiffusionModule. Applies a sequence of noise and denoising operations to
+        the input coordinates to simulate the diffusion process.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, num_atoms, channels)
+                            representing the atomic features including coordinates.
+
+        Returns:
+            torch.Tensor: Output tensor of shape (batch_size, num_atoms, channels) with
+                        denoised atom coordinates.
+        """
+        batch_size, num_atoms, channels = x.shape
+        noisy_x = x.clone()
+
+        for step in range(self.num_diffusion_steps):
+            # Generate noise scaled by the noise level for the current step
+            noise_level = self.noise_scale[step]
+            noise = (
+                torch.randn(
+                    batch_size, num_atoms, channels, device=x.device
+                )
+                * noise_level
+            )
+
+            # Add noise to the input
+            noisy_x = x + noise
+
+            # Predict and denoise the noisy input
+            noisy_x = self.prediction_network(noisy_x)
+
+        if self.training and ground_truth is not None:
+            loss = F.mse_loss(noisy_x, ground_truth)
+            return noisy_x, loss
+
+        return noisy_x
+
+
+# Example usage
+if __name__ == "__main__":
+    model = GeneticDiffusionModule(
+        channels=3
+    )  # Assuming 3D coordinates
+    input_coords = torch.randn(
+        10, 100, 3
+    )  # Example with batch size 10 and 100 atoms
+    output_coords = model(input_coords)
+    print(output_coords.shape)  # Should be (10, 100, 3)