Source code for atomgen.models.tokengt

"""Implementation of the TokenGT model."""

from typing import Any, Callable, Optional, Tuple

import torch
import torch.nn.functional as f
from torch import nn
from torch.utils.checkpoint import checkpoint
from transformers import PretrainedConfig, PreTrainedModel


ATOM_METADATA = [
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class ParallelBlock(nn.Module):
    """Parallel transformer block."""

    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_ratio: int = 4,
        dropout: float = 0.0,
    ):
        super().__init__()
        assert (
            dim % num_heads == 0
        ), f"dim {dim} should be divisible by num_heads {num_heads}"
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim**-0.5
        self.mlp_hidden_dim = int(mlp_ratio * dim)
        self.dropout = dropout
        self.proj_drop = nn.Dropout(self.dropout)

        self.in_proj_in_dim = dim
        self.in_proj_out_dim = self.mlp_hidden_dim + 3 * dim
        self.out_proj_in_dim = self.mlp_hidden_dim + dim
        self.out_proj_out_dim = 2 * dim

        self.in_split = [self.mlp_hidden_dim] + [dim] * 3
        self.out_split = [dim] * 2

        self.in_norm = nn.LayerNorm(dim)
        self.q_norm = nn.LayerNorm(self.head_dim)
        self.k_norm = nn.LayerNorm(self.head_dim)
        self.in_proj = nn.Linear(self.in_proj_in_dim, self.in_proj_out_dim, bias=False)
        self.in_proj = nn.Linear(dim, dim * mlp_ratio)
        self.act_fn = nn.GELU()
        self.out_proj = nn.Linear(
            self.out_proj_in_dim, self.out_proj_out_dim, bias=False
        )



    def forward(
        self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None
    ) -> torch.Tensor:
        """Forward function call for the parallel transformer block."""
        b, n, c = x.shape
        res = x
        x = self.in_norm(x)

        x = self.in_proj(self.in_norm(x))

        x, q, k, v = torch.split(x, self.in_split, dim=-1)
        x = self.act_fn(x)
        x = self.proj_drop(x)

        q = self.q_norm(q.view(b, n, self.num_heads, self.head_dim).transpose(1, 2))
        k = self.k_norm(k.view(b, n, self.num_heads, self.head_dim).transpose(1, 2))
        v = v.view(b, n, self.num_heads, self.head_dim).transpose(1, 2)

        x_attn = (
            f.scaled_dot_product_attention(
                q, k, v, attn_mask=attention_mask, dropout_p=self.dropout
            )
            .transpose(1, 2)
            .reshape(b, n, c)
        )

        x_mlp, x_attn = self.out_proj(torch.cat([x, x_attn], dim=-1)).split(
            self.out_split, dim=-1
        )
        out: torch.Tensor = x_mlp + x_attn + res

        return out






class TransformerConfig(PretrainedConfig):  # type: ignore
    """Configuration class to store the configuration of a TokenGT model."""

    def __init__(
        self,
        vocab_size: int = 123,
        dim: int = 768,
        num_heads: int = 12,
        depth: int = 12,
        mlp_ratio: int = 4,
        k: int = 16,
        sigma: float = 0.03,
        type_id_dim: int = 64,
        dropout: float = 0.0,
        mask_token_id: int = 0,
        pad_token_id: int = 119,
        bos_token_id: int = 120,
        eos_token_id: int = 121,
        cls_token_id: int = 122,
        gradient_checkpointing: bool = False,
        **kwargs: Any,
    ):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.dim = dim
        self.num_heads = num_heads
        self.depth = depth
        self.mlp_ratio = mlp_ratio
        self.k = k
        self.sigma = sigma
        self.type_id_dim = type_id_dim
        self.dropout = dropout
        self.mask_token_id = mask_token_id
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.cls_token_id = cls_token_id
        self.gradient_checkpointing = gradient_checkpointing





class TransformerEncoder(nn.Module):
    """Transformer encoder for atom modeling."""

    def __init__(self, config: TransformerConfig):
        super().__init__()
        self.vocab_size = config.vocab_size
        self.dim = config.dim
        self.num_heads = config.num_heads
        self.depth = config.depth
        self.mlp_ratio = config.mlp_ratio
        self.k = config.k
        self.sigma = config.sigma
        self.type_id_dim = config.type_id_dim
        self.gradient_checkpointing = config.gradient_checkpointing
        self.dropout = config.dropout
        self.metadata_vocab = nn.Embedding(122, 17)
        vocab_weight = torch.empty(122, 17).fill_(-1.0)
        vocab_weight[2:-2] = torch.tensor(ATOM_METADATA, dtype=torch.float32)
        self.metadata_vocab.weight = nn.Parameter(vocab_weight, requires_grad=False)
        self.node_id = nn.Embedding(1, self.type_id_dim)
        self.edge_id = nn.Embedding(1, self.type_id_dim)
        self.embed_proj = nn.Linear(17 + 2 * self.k + self.type_id_dim, self.dim)
        self.graph = nn.Embedding(1, self.dim)
        self.distance = nn.Embedding(1, 17)
        self.distance_norm = nn.LayerNorm(17)

        self.blocks = nn.ModuleList()
        for _ in range(self.depth):
            self.blocks.append(
                ParallelBlock(self.dim, self.num_heads, self.mlp_ratio, self.dropout)
            )

    def _expand_mask(
        self,
        mask: torch.Tensor,
        dtype: torch.dtype,
        device: torch.device,
        tgt_len: Optional[int] = None,
    ) -> torch.Tensor:
        bsz, src_len = mask.size()
        tgt_len = tgt_len if tgt_len is not None else src_len

        expanded_mask = (
            mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
        )

        inverted_mask: torch.Tensor = 1.0 - expanded_mask

        return inverted_mask.masked_fill(
            inverted_mask.to(torch.bool), torch.finfo(dtype).min
        ).to(device)



    def forward(
        self,
        input_ids: torch.Tensor,
        coords: torch.Tensor,
        node_pe: torch.Tensor,
        edge_pe: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Forward function call for the transformer encoder."""
        atom_metadata = self.metadata_vocab(input_ids)  # (B, N, 17)

        node_ids = self.node_id(
            torch.zeros(
                node_pe.size(0),
                node_pe.size(1),
                dtype=torch.long,
                device=node_pe.device,
            )
        )
        edge_ids = self.edge_id(
            torch.zeros(
                edge_pe.size(0),
                edge_pe.size(1),
                dtype=torch.long,
                device=edge_pe.device,
            )
        )

        graph_tokens = self.graph(
            torch.zeros(node_pe.size(0), 1, dtype=torch.long, device=node_pe.device)
        )

        nodes = torch.cat([atom_metadata, node_pe, node_ids], dim=-1)
        distance_embed = self.distance_norm(
            self.distance(
                torch.zeros(
                    edge_pe.size(0),
                    edge_pe.size(1),
                    dtype=torch.long,
                    device=edge_pe.device,
                )
            )
            * edge_pe[:, :, -1:]
        )
        edges = torch.cat([distance_embed, edge_pe[:, :, :-1], edge_ids], dim=-1)

        input_embeds: torch.Tensor = self.embed_proj(torch.cat([nodes, edges], dim=1))
        input_embeds = torch.cat([graph_tokens, input_embeds], dim=1)

        # convert attention mask from long into Boolean and add ones for graph token
        attention_mask = (
            torch.cat(
                [
                    torch.ones(
                        attention_mask.size(0),
                        1,
                        dtype=torch.bool,
                        device=attention_mask.device,
                    ),
                    attention_mask.bool(),
                ],
                dim=1,
            )
            if attention_mask is not None
            else None
        )

        for blk in self.blocks:
            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module: Any) -> Callable[..., Any]:
                    def custom_forward(*inputs: Any) -> Any:
                        return module(*inputs)

                    return custom_forward

                input_embeds = checkpoint(
                    create_custom_forward(blk),
                    input_embeds,
                    attention_mask,
                )
            else:
                input_embeds = blk(input_embeds, attention_mask)
        return input_embeds






class TransformerPreTrainedModel(PreTrainedModel):  # type: ignore
    """Base class for all transformer models."""

    config_class = TransformerConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["ParallelBlock"]

    def _set_gradient_checkpointing(
        self, module: nn.Module, value: bool = False
    ) -> None:
        if isinstance(module, (TransformerEncoder)):
            module.gradient_checkpointing = value





class TransformerModel(TransformerPreTrainedModel):
    """Transformer model for atom modeling."""

    def __init__(self, config: TransformerConfig):
        super().__init__(config)
        self.config = config
        self.encoder = TransformerEncoder(config)



    def forward(
        self,
        input_ids: torch.Tensor,
        coords: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Forward function call for the transformer model."""
        out: torch.Tensor = self.encoder(input_ids, coords, attention_mask)
        return out






class TransformerForMaskedAM(TransformerPreTrainedModel):
    """Transformer with an atom modeling head on top for masked atom modeling."""

    def __init__(self, config: TransformerConfig):
        super().__init__(config)
        self.config = config
        self.encoder = TransformerEncoder(config)
        self.am_head = nn.Linear(config.dim, config.vocab_size, bias=False)



    def forward(
        self,
        input_ids: torch.Tensor,
        coords: torch.Tensor,
        labels: Optional[torch.Tensor] = None,
        fixed: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> Tuple[Optional[torch.Tensor], torch.Tensor]:
        """Forward function call for the masked atom modeling model."""
        hidden_states = self.encoder(input_ids, coords, attention_mask)
        logits = self.am_head(hidden_states[:, 1 : input_ids.size(1)])

        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            logits, labels = logits.view(-1, self.config.vocab_size), labels.view(-1)
            loss = loss_fct(logits, labels)

        return loss, logits






class TransformerForCoordinateAM(TransformerPreTrainedModel):
    """Transformer with an atom coordinate head on top for coordinate denoising."""

    def __init__(self, config: TransformerConfig):
        super().__init__(config)
        self.config = config
        self.encoder = TransformerEncoder(config)
        self.coords_head = nn.Linear(config.dim, 3)



    def forward(
        self,
        input_ids: torch.Tensor,
        coords: torch.Tensor,
        labels_coords: Optional[torch.Tensor] = None,
        fixed: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> Tuple[Optional[torch.Tensor], torch.Tensor]:
        """Forward function call for the coordinate atom modeling model."""
        hidden_states = self.encoder(input_ids, coords, attention_mask)
        coords_pred = self.coords_head(hidden_states[:, 1 : input_ids.size(1)])

        loss = None
        if labels_coords is not None:
            labels_coords = labels_coords.to(coords_pred.device)
            loss_fct = nn.L1Loss()
            loss = loss_fct(coords_pred, labels_coords)

        return loss, coords_pred






class InitialStructure2RelaxedStructure(TransformerPreTrainedModel):
    """Transformer with an coordinate head on top for relaxed structure prediction."""

    def __init__(self, config: TransformerConfig):
        super().__init__(config)
        self.config = config
        self.encoder = TransformerEncoder(config)
        self.coords_head = nn.Linear(config.dim, 3)



    def forward(
        self,
        input_ids: torch.Tensor,
        coords: torch.Tensor,
        labels_coords: Optional[torch.Tensor] = None,
        fixed: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> Tuple[Optional[torch.Tensor], torch.Tensor]:
        """Forward function call.

        Initial structure to relaxed structure model.
        """
        hidden_states = self.encoder(input_ids, coords, attention_mask)
        coords_pred = self.coords_head(hidden_states[:, 1 : input_ids.size(1)])

        loss = None
        if labels_coords is not None:
            labels_coords = labels_coords.to(coords_pred.device)
            loss_fct = nn.L1Loss()
            loss = loss_fct(coords_pred, labels_coords)

        return loss, coords_pred






class InitialStructure2RelaxedEnergy(TransformerPreTrainedModel):
    """Transformer with an energy head on top for relaxed energy prediction."""

    def __init__(self, config: TransformerConfig):
        super().__init__(config)
        self.config = config
        self.encoder = TransformerEncoder(config)
        self.energy_norm = nn.LayerNorm(config.dim)
        self.energy_head = nn.Linear(config.dim, 1, bias=False)



    def forward(
        self,
        input_ids: torch.Tensor,
        coords: torch.Tensor,
        labels_energy: Optional[torch.Tensor] = None,
        fixed: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> Tuple[Optional[torch.Tensor], torch.Tensor]:
        """Forward function call for the initial structure to relaxed energy model."""
        hidden_states = self.encoder(input_ids, coords, attention_mask)
        energy = self.energy_head(self.energy_norm(hidden_states[:, 0])).squeeze(-1)

        loss = None
        if labels_energy is not None:
            loss_fct = nn.L1Loss()
            loss = loss_fct(energy, labels_energy)

        return loss, energy






class InitialStructure2RelaxedStructureAndEnergy(TransformerPreTrainedModel):
    """Initial structure to relaxed structure and energy prediction model.

    Transformer with an coordinate and energy head on top for
    relaxed structure and energy prediction.
    """

    def __init__(self, config: TransformerConfig):
        super().__init__(config)
        self.config = config
        self.encoder = TransformerEncoder(config)
        self.coords_head = nn.Linear(config.dim, 3)
        self.energy_norm = nn.LayerNorm(config.dim)
        self.energy_head = nn.Linear(config.dim, 1, bias=False)



    def forward(
        self,
        input_ids: torch.Tensor,
        coords: torch.Tensor,
        labels_coords: Optional[torch.Tensor] = None,
        labels_energy: Optional[torch.Tensor] = None,
        fixed: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        """Forward function call.

        Initial structure to relaxed structure and energy model.
        """
        hidden_states = self.encoder(input_ids, coords, attention_mask)
        coords_pred: torch.Tensor = self.coords_head(
            hidden_states[:, 1 : input_ids.size(1)]
        )
        energy: torch.Tensor = self.energy_head(
            self.energy_norm(hidden_states[:, 0])
        ).squeeze(-1)

        loss_coords = torch.tensor(0.0, device=input_ids.device)
        if labels_coords is not None:
            labels_coords = labels_coords.to(coords_pred.device)
            loss_fct = nn.L1Loss()
            loss_coords = loss_fct(coords_pred, labels_coords)

        loss_energy = torch.tensor(0.0, device=input_ids.device)
        if labels_energy is not None:
            loss_fct = nn.L1Loss()
            loss_energy = loss_fct(energy, labels_energy)

        loss = loss_coords + loss_energy

        return loss, (coords_pred, energy)






class Structure2EnergyAndForces(TransformerPreTrainedModel):
    """Structure to energy and forces prediction model.

    Transformer with an energy and forces head on top for energy and forces prediction.
    """

    def __init__(self, config: TransformerConfig):
        super().__init__(config)
        self.config = config
        self.encoder = TransformerEncoder(config)
        self.force_norm = nn.LayerNorm(config.dim)
        self.force_head = nn.Linear(config.dim, 3)
        self.energy_norm = nn.LayerNorm(config.dim)
        self.formation_energy_head = nn.Linear(config.dim, 1, bias=False)



    def forward(
        self,
        input_ids: torch.Tensor,
        coords: torch.Tensor,
        forces: Optional[torch.Tensor] = None,
        total_energy: Optional[torch.Tensor] = None,
        formation_energy: Optional[torch.Tensor] = None,
        has_formation_energy: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        node_pe: Optional[torch.Tensor] = None,
        edge_pe: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]]:
        """Forward function call for the structure to energy and forces model."""
        hidden_states = self.encoder(
            input_ids, coords, attention_mask, node_pe, edge_pe
        )

        formation_energy_pred: torch.Tensor = self.formation_energy_head(
            self.energy_norm(hidden_states[:, 0])
        ).squeeze(-1)

        loss_formation_energy = torch.Tensor(0.0, device=input_ids.device)
        if formation_energy is not None:
            loss_fct = nn.L1Loss()
            loss_formation_energy = loss_fct(
                formation_energy_pred[has_formation_energy],
                formation_energy[has_formation_energy],
            )

        forces_pred: torch.Tensor = self.force_head(
            self.force_norm(hidden_states[:, 1 : input_ids.size(1)])
        )
        loss_forces = torch.Tensor(0.0, device=input_ids.device)
        if forces is not None and attention_mask is not None:
            loss_fct = nn.L1Loss()
            loss_forces = loss_fct(
                forces_pred[attention_mask[:, 1 : input_ids.size(1)].bool()],
                forces[attention_mask[:, 1 : input_ids.size(1)].bool()],
            )

        loss = loss_formation_energy + loss_forces

        return loss, (
            formation_energy_pred,
            forces_pred,
            attention_mask.bool() if attention_mask is not None else attention_mask,
        )