complete the main alphafold2 flow, sans diffusion module and losses +…

… sampling

README.md

@@ -6,6 +6,56 @@ Implementation of <a href="https://www.nature.com/articles/s41586-024-07487-w">A
 
 Getting a fair number of emails. You can chat with me about this work <a href="https://discord.gg/x6FuzQPQXY">here</a>
 
+## Install
+
+```bash
+$ pip install alphafold3-pytorch
+```
+
+## Usage
+
+```python
+import torch
+from alphafold3_pytorch import Alphafold3
+
+alphafold3 = Alphafold3(
+    dim_atom_inputs = 77,
+    dim_additional_residue_feats = 33,
+    dim_template_feats = 44
+)
+
+# mock inputs
+
+seq_len = 16
+atom_seq_len = seq_len * 27
+
+atom_inputs = torch.randn(2, atom_seq_len, 77)
+atom_mask = torch.ones((2, atom_seq_len)).bool()
+atompair_feats = torch.randn(2, atom_seq_len, atom_seq_len, 16)
+additional_residue_feats = torch.randn(2, seq_len, 33)
+
+template_feats = torch.randn(2, 2, seq_len, seq_len, 44)
+template_mask = torch.ones((2, 2)).bool()
+
+msa = torch.randn(2, 7, seq_len, 64)
+
+# train
+
+loss = alphafold3(
+    num_recycling_steps = 2,
+    atom_inputs = atom_inputs,
+    atom_mask = atom_mask,
+    atompair_feats = atompair_feats,
+    additional_residue_feats = additional_residue_feats,
+    msa = msa,
+    templates = template_feats,
+    template_mask = template_mask
+)
+
+loss.backward()
+
+```
+
 ## Citations
 
 ```bibtex

alphafold3_pytorch/alphafold3.py

@@ -7,6 +7,7 @@
 i - residue sequence length (source)
 j - residue sequence length (target)
 m - atom sequence length
+c - coordinates (3 for spatial)
 d - feature dimension
 ds - feature dimension (single)
 dp - feature dimension (pairwise)
@@ -861,7 +862,7 @@ def __init__(
         # final projection of mean pooled repr -> out
 
         self.to_out = nn.Sequential(
-            LinearNoBias(dim, dim),
+            LinearNoBias(dim, dim_pairwise),
             nn.ReLU()
         )
 
@@ -873,7 +874,7 @@ def forward(
         template_mask: Bool['b t'],
         pairwise_repr: Float['b n n dp'],
         mask: Bool['b n'] | None = None,
-    ) -> Float['b n n d']:
+    ) -> Float['b n n dp']:
 
         num_templates = templates.shape[1]
 
@@ -884,7 +885,8 @@ def forward(
 
         v, merged_batch_ps = pack_one(v, '* i j d')
 
-        mask = repeat(mask, 'b n -> (b t) n', t = num_templates)
+        if exists(mask):
+            mask = repeat(mask, 'b n -> (b t) n', t = num_templates)
 
         for block in self.pairformer_stack:
             v = block(
@@ -1815,7 +1817,7 @@ def forward(
         pairwise_repr: Float['b n n dp'],
         pred_atom_pos: Float['b n c'],
         mask: Bool['b n'] | None = None,
-        calc_pae_logits_and_loss = True
+        return_pae_logits = True
 
     ) -> ConfidenceHeadLogits[
         Float['b pae n n'] | None,
@@ -1854,7 +1856,7 @@ def forward(
 
         pae_logits = None
 
-        if calc_pae_logits_and_loss:
+        if return_pae_logits:
             pae_logits = self.to_pae_logits(pairwise_repr)
 
         # return all logits
@@ -1863,21 +1865,248 @@ def forward(
 
 # main class
 
+LossBreakdown = namedtuple('LossBreakdown', [
+    'distogram',
+    'pae',
+    'pdt',
+    'plddt',
+    'resolved'
+])
+
 class Alphafold3(Module):
+    """ Algorithm 1 """
+
+    @typecheck
     def __init__(
         self,
         *,
+        dim_atom_inputs,
+        dim_additional_residue_feats,
+        dim_template_feats,
+        dim_template_model = 64,
+        atoms_per_window = 27,
+        dim_atom = 128,
+        dim_atompair = 16,
+        dim_input_embedder_token = 384,
+        dim_single = 384,
+        dim_pairwise = 128,
+        atompair_dist_bins: Float[' dist_bins'] = torch.linspace(3, 20, 37),
+        ignore_index = -1,
+        num_dist_bins = 38,
+        num_plddt_bins = 50,
+        num_pde_bins = 64,
+        num_pae_bins = 64,
         loss_confidence_weight = 1e-4,
         loss_distogram_weight = 1e-2,
-        loss_diffusion = 4.
+        loss_diffusion_weight = 4.,
+        input_embedder_kwargs: dict = dict(
+            atom_transformer_blocks = 3,
+            atom_transformer_heads = 4,
+            atom_transformer_kwargs = dict()
+        ),
+        confidence_head_kwargs: dict = dict(
+            pairformer_depth = 4
+        ),
+        template_embedder_kwargs: dict = dict(
+            pairformer_stack_depth = 2,
+            pairwise_block_kwargs = dict(),
+        ),
+        msa_module_kwargs: dict = dict(
+            depth = 4,
+            dim_msa = 64,
+            dim_msa_input = None,
+            outer_product_mean_dim_hidden = 32,
+            msa_pwa_dropout_row_prob = 0.15,
+            msa_pwa_heads = 8,
+            msa_pwa_dim_head = 32,
+            pairwise_block_kwargs = dict()
+        ),
+        pairformer_stack: dict = dict(
+            depth = 48,
+            pair_bias_attn_dim_head = 64,
+            pair_bias_attn_heads = 16,
+            dropout_row_prob = 0.25,
+            pairwise_block_kwargs = dict()
+        )
     ):
         super().__init__()
 
+        self.atoms_per_window = atoms_per_window
+
+        # input feature embedder
+
+        self.input_embedder = InputFeatureEmbedder(
+            dim_atom_inputs = dim_atom_inputs,
+            dim_additional_residue_feats = dim_additional_residue_feats,
+            atoms_per_window = atoms_per_window,
+            dim_atom = dim_atom,
+            dim_atompair = dim_atompair,
+            dim_token = dim_input_embedder_token,
+            dim_single = dim_single,
+            dim_pairwise = dim_pairwise,
+            **input_embedder_kwargs
+        )
+
+        dim_single_inputs = dim_input_embedder_token + dim_additional_residue_feats
+
+        # templates
+
+        self.template_embedder = TemplateEmbedder(
+            dim_template_feats = dim_template_feats,
+            dim = dim_template_model,
+            dim_pairwise = dim_pairwise,
+            **template_embedder_kwargs
+        )
+
+        # msa
+
+        self.msa_module = MSAModule(
+            dim_single = dim_single,
+            dim_pairwise = dim_pairwise,
+            **msa_module_kwargs
+        )
+
+        # main pairformer trunk, 48 layers
+
+        self.pairformer = PairformerStack(
+            dim_single = dim_single,
+            dim_pairwise = dim_pairwise,
+            **pairformer_stack
+        )
+
+        # recycling related
+
+        self.recycle_single = nn.Sequential(
+            nn.LayerNorm(dim_single),
+            LinearNoBias(dim_single, dim_single)
+        )
+
+        self.recycle_pairwise = nn.Sequential(
+            nn.LayerNorm(dim_pairwise),
+            LinearNoBias(dim_pairwise, dim_pairwise)
+        )
+
+        # logit heads
+
+        self.distogram_head = DistogramHead(
+            dim_pairwise = dim_pairwise,
+            num_dist_bins = num_dist_bins
+        )
+
+        self.confidence_head = ConfidenceHead(
+            dim_single_inputs = dim_single_inputs,
+            atompair_dist_bins = atompair_dist_bins,
+            dim_single = dim_single,
+            dim_pairwise = dim_pairwise,
+            num_plddt_bins = num_plddt_bins,
+            num_pde_bins = num_pde_bins,
+            num_pae_bins = num_pae_bins,
+            **confidence_head_kwargs
+        )
+
+        # loss related
+
+        self.ignore_index = ignore_index
+        self.loss_distogram_weight = loss_distogram_weight
+        self.loss_confidence_weight = loss_confidence_weight
+        self.loss_diffusion_weight = loss_diffusion_weight
 
     @typecheck
     def forward(
         self,
         *,
-        include_pae_loss = False # turned on in latter part of training
-    ):
-        return
+        atom_inputs: Float['b m dai'],
+        atom_mask: Bool['b m'],
+        atompair_feats: Float['b m m dap'],
+        additional_residue_feats: Float['b n rf'],
+        msa: Float['b s n d'],
+        templates: Float['b t n n dt'],
+        template_mask: Bool['b t'],
+        num_recycling_steps: int = 1,
+        distance_labels: Int['b n n'] | None = None,
+        pae_labels: Int['b n n'] | None = None,
+        pde_labels: Int['b n n'] | None = None,
+        plddt_labels: Int['b n'] | None = None,
+        resolved_labels: Int['b n'] | None = None,
+    ) -> Float['b m c'] | Float['']:
+
+        # embed inputs
+
+        (
+            single_inputs,
+            single_init,
+            pairwise_init,
+            atom_feats,
+            atompair_feats
+        ) = self.input_embedder(
+            atom_inputs = atom_inputs,
+            atom_mask = atom_mask,
+            atompair_feats = atompair_feats,
+            additional_residue_feats = additional_residue_feats
+        )
+
+        w = self.atoms_per_window
+
+        mask = reduce(atom_mask, 'b (n w) -> b n', w = w, reduction = 'any')
+
+        # init recycled single and pairwise
+
+        recycled_pairwise = recycled_single = None
+        single = pairwise = None
+
+        # for each recycling step
+
+        for _ in range(num_recycling_steps):
+
+            # handle recycled single and pairwise if not first step
+
+            recycled_single = recycled_pairwise = 0.
+
+            if exists(single):
+                recycled_single = self.recycle_single(single)
+
+            if exists(pairwise):
+                recycled_pairwise = self.recycle_pairwise(pairwise)
+
+            single = single_init + recycled_single
+            pairwise = pairwise_init + recycled_pairwise
+
+            # else go through main transformer trunk from alphafold2
+
+            # templates
+
+            embedded_template = self.template_embedder(
+                templates = templates,
+                template_mask = template_mask,
+                pairwise_repr = pairwise,
+                mask = mask
+            )
+
+            pairwise = embedded_template + pairwise
+
+            # msa
+
+            embedded_msa = self.msa_module(
+                msa = msa,
+                single_repr = single,
+                pairwise_repr = pairwise,
+                mask = mask
+            )
+
+            pairwise = embedded_msa + pairwise
+
+            # main attention trunk (pairformer)
+
+            single, pairwise = self.pairformer(
+                single_repr = single,
+                pairwise_repr = pairwise,
+                mask = mask
+            )
+
+        # determine whether to return loss if any labels were to be passed in
+        # otherwise will sample the atomic coordinates
+
+        labels = (distance_labels, pae_labels, pde_labels, plddt_labels, resolved_labels)
+        return_loss = any([*filter(exists, labels)])
+
+        return torch.tensor(0.)