Merge pull request #47 from basf/restructure

Restructure

mambular/arch_utils/mamba_arch.py

@@ -41,6 +41,8 @@ def __init__(
         dt_init_floor=1e-04,
         norm=RMSNorm,
         activation=F.silu,
+        bidirectional=False,
+        use_learnable_interaction=False,
     ):
         super().__init__()
 
@@ -62,6 +64,8 @@ def __init__(
                     dt_init_floor,
                     norm,
                     activation,
+                    bidirectional,
+                    use_learnable_interaction,
                 )
                 for _ in range(n_layers)
             ]
@@ -99,6 +103,8 @@ def __init__(
         dt_init_floor=1e-04,
         norm=RMSNorm,
         activation=F.silu,
+        bidirectional=False,
+        use_learnable_interaction=False,
     ):
         super().__init__()
 
@@ -141,6 +147,8 @@ def __init__(
             dt_min=dt_min,
             dt_init_floor=dt_init_floor,
             activation=activation,
+            bidirectional=bidirectional,
+            use_learnable_interaction=use_learnable_interaction,
         )
         self.norm = norm(d_model)
 
@@ -153,14 +161,14 @@ class MambaBlock(nn.Module):
     """MambaBlock module containing the main computational components.
 
     Attributes:
-        config (MambularConfig): Configuration object for the MambaBlock.
         in_proj (nn.Linear): Linear projection for input.
         conv1d (nn.Conv1d): 1D convolutional layer.
         x_proj (nn.Linear): Linear projection for input-dependent tensors.
         dt_proj (nn.Linear): Linear projection for dynamical time.
         A_log (nn.Parameter): Logarithmically stored A tensor.
         D (nn.Parameter): Tensor for D component.
         out_proj (nn.Linear): Linear projection for output.
+        learnable_interaction (LearnableFeatureInteraction): Learnable feature interaction layer.
     """
 
     def __init__(
@@ -179,88 +187,154 @@ def __init__(
         dt_min=1e-03,
         dt_init_floor=1e-04,
         activation=F.silu,
+        bidirectional=False,
+        use_learnable_interaction=False,
     ):
         super().__init__()
         self.d_inner = d_model * expand_factor
+        self.bidirectional = bidirectional
+        self.use_learnable_interaction = use_learnable_interaction
 
-        self.in_proj = nn.Linear(d_model, 2 * self.d_inner, bias=bias)
+        self.in_proj_fwd = nn.Linear(d_model, 2 * self.d_inner, bias=bias)
+        if self.bidirectional:
+            self.in_proj_bwd = nn.Linear(d_model, 2 * self.d_inner, bias=bias)
 
-        self.conv1d = nn.Conv1d(
+        self.conv1d_fwd = nn.Conv1d(
             in_channels=self.d_inner,
             out_channels=self.d_inner,
             kernel_size=d_conv,
             bias=conv_bias,
             groups=self.d_inner,
             padding=d_conv - 1,
         )
+        if self.bidirectional:
+            self.conv1d_bwd = nn.Conv1d(
+                in_channels=self.d_inner,
+                out_channels=self.d_inner,
+                kernel_size=d_conv,
+                bias=conv_bias,
+                groups=self.d_inner,
+                padding=d_conv - 1,
+            )
 
         self.dropout = nn.Dropout(dropout)
         self.activation = activation
 
-        self.x_proj = nn.Linear(self.d_inner, dt_rank + 2 * d_state, bias=False)
+        if self.use_learnable_interaction:
+            self.learnable_interaction = LearnableFeatureInteraction(self.d_inner)
+
+        self.x_proj_fwd = nn.Linear(self.d_inner, dt_rank + 2 * d_state, bias=False)
+        if self.bidirectional:
+            self.x_proj_bwd = nn.Linear(self.d_inner, dt_rank + 2 * d_state, bias=False)
 
-        self.dt_proj = nn.Linear(dt_rank, self.d_inner, bias=True)
+        self.dt_proj_fwd = nn.Linear(dt_rank, self.d_inner, bias=True)
+        if self.bidirectional:
+            self.dt_proj_bwd = nn.Linear(dt_rank, self.d_inner, bias=True)
 
         dt_init_std = dt_rank**-0.5 * dt_scale
         if dt_init == "constant":
-            nn.init.constant_(self.dt_proj.weight, dt_init_std)
+            nn.init.constant_(self.dt_proj_fwd.weight, dt_init_std)
+            if self.bidirectional:
+                nn.init.constant_(self.dt_proj_bwd.weight, dt_init_std)
         elif dt_init == "random":
-            nn.init.uniform_(self.dt_proj.weight, -dt_init_std, dt_init_std)
+            nn.init.uniform_(self.dt_proj_fwd.weight, -dt_init_std, dt_init_std)
+            if self.bidirectional:
+                nn.init.uniform_(self.dt_proj_bwd.weight, -dt_init_std, dt_init_std)
         else:
             raise NotImplementedError
 
-        dt = torch.exp(
+        dt_fwd = torch.exp(
             torch.rand(self.d_inner) * (math.log(dt_max) - math.log(dt_min))
             + math.log(dt_min)
         ).clamp(min=dt_init_floor)
-        inv_dt = dt + torch.log(-torch.expm1(-dt))
+        inv_dt_fwd = dt_fwd + torch.log(-torch.expm1(-dt_fwd))
         with torch.no_grad():
-            self.dt_proj.bias.copy_(inv_dt)
+            self.dt_proj_fwd.bias.copy_(inv_dt_fwd)
+
+        if self.bidirectional:
+            dt_bwd = torch.exp(
+                torch.rand(self.d_inner) * (math.log(dt_max) - math.log(dt_min))
+                + math.log(dt_min)
+            ).clamp(min=dt_init_floor)
+            inv_dt_bwd = dt_bwd + torch.log(-torch.expm1(-dt_bwd))
+            with torch.no_grad():
+                self.dt_proj_bwd.bias.copy_(inv_dt_bwd)
 
         A = torch.arange(1, d_state + 1, dtype=torch.float32).repeat(self.d_inner, 1)
-        self.A_log = nn.Parameter(torch.log(A))
-        self.D = nn.Parameter(torch.ones(self.d_inner))
+        self.A_log_fwd = nn.Parameter(torch.log(A))
+        if self.bidirectional:
+            self.A_log_bwd = nn.Parameter(torch.log(A))
+
+        self.D_fwd = nn.Parameter(torch.ones(self.d_inner))
+        if self.bidirectional:
+            self.D_bwd = nn.Parameter(torch.ones(self.d_inner))
+
         self.out_proj = nn.Linear(self.d_inner, d_model, bias=bias)
         self.dt_rank = dt_rank
         self.d_state = d_state
 
     def forward(self, x):
         _, L, _ = x.shape
 
-        xz = self.in_proj(x)
-        x, z = xz.chunk(2, dim=-1)
+        xz_fwd = self.in_proj_fwd(x)
+        x_fwd, z_fwd = xz_fwd.chunk(2, dim=-1)
 
-        x = x.transpose(1, 2)
-        x = self.conv1d(x)[:, :, :L]
-        x = x.transpose(1, 2)
+        x_fwd = x_fwd.transpose(1, 2)
+        x_fwd = self.conv1d_fwd(x_fwd)[:, :, :L]
+        x_fwd = x_fwd.transpose(1, 2)
 
-        x = self.activation(x)
-        x = self.dropout(x)
-        y = self.ssm(x)
+        if self.bidirectional:
+            xz_bwd = self.in_proj_bwd(x)
+            x_bwd, z_bwd = xz_bwd.chunk(2, dim=-1)
 
-        z = self.activation(z)
-        z = self.dropout(z)
+            x_bwd = x_bwd.transpose(1, 2)
+            x_bwd = self.conv1d_bwd(x_bwd)[:, :, :L]
+            x_bwd = x_bwd.transpose(1, 2)
 
-        output = y * z
-        output = self.out_proj(output)
+        if self.use_learnable_interaction:
+            x_fwd = self.learnable_interaction(x_fwd)
+            if self.bidirectional:
+                x_bwd = self.learnable_interaction(x_bwd)
 
-        return output
+        x_fwd = self.activation(x_fwd)
+        x_fwd = self.dropout(x_fwd)
+        y_fwd = self.ssm(x_fwd, forward=True)
 
-    def ssm(self, x):
-        A = -torch.exp(self.A_log.float())
-        D = self.D.float()
+        if self.bidirectional:
+            x_bwd = self.activation(x_bwd)
+            x_bwd = self.dropout(x_bwd)
+            y_bwd = self.ssm(torch.flip(x_bwd, [1]), forward=False)
+            y = y_fwd + torch.flip(y_bwd, [1])
+        else:
+            y = y_fwd
 
-        deltaBC = self.x_proj(x)
+        z_fwd = self.activation(z_fwd)
+        z_fwd = self.dropout(z_fwd)
 
-        delta, B, C = torch.split(
-            deltaBC,
-            [self.dt_rank, self.d_state, self.d_state],
-            dim=-1,
-        )
-        delta = F.softplus(self.dt_proj(delta))
+        output = y * z_fwd
+        output = self.out_proj(output)
 
-        y = self.selective_scan_seq(x, delta, A, B, C, D)
+        return output
+
+    def ssm(self, x, forward=True):
+        if forward:
+            A = -torch.exp(self.A_log_fwd.float())
+            D = self.D_fwd.float()
+            deltaBC = self.x_proj_fwd(x)
+            delta, B, C = torch.split(
+                deltaBC, [self.dt_rank, self.d_state, self.d_state], dim=-1
+            )
+            delta = F.softplus(self.dt_proj_fwd(delta))
+        else:
+            A = -torch.exp(self.A_log_bwd.float())
+            D = self.D_bwd.float()
+            deltaBC = self.x_proj_bwd(x)
+            delta, B, C = torch.split(
+                deltaBC, [self.dt_rank, self.d_state, self.d_state], dim=-1
+            )
+            delta = F.softplus(self.dt_proj_bwd(delta))
 
+        y = self.selective_scan_seq(x, delta, A, B, C, D)
         return y
 
     def selective_scan_seq(self, x, delta, A, B, C, D):
@@ -285,3 +359,15 @@ def selective_scan_seq(self, x, delta, A, B, C, D):
         y = y + D * x
 
         return y
+
+
+class LearnableFeatureInteraction(nn.Module):
+    def __init__(self, n_vars):
+        super().__init__()
+        self.interaction_weights = nn.Parameter(torch.Tensor(n_vars, n_vars))
+        nn.init.xavier_uniform_(self.interaction_weights)
+
+    def forward(self, x):
+        batch_size, n_vars, d_model = x.size()
+        interactions = torch.matmul(x, self.interaction_weights)
+        return interactions.view(batch_size, n_vars, d_model)

mambular/base_models/mambular.py

@@ -105,6 +105,10 @@ def __init__(
             dt_init_floor=self.hparams.get("dt_init_floor", config.dt_init_floor),
             norm=globals()[self.hparams.get("norm", config.norm)],
             activation=self.hparams.get("activation", config.activation),
+            bidirectional=self.hparams.get("bidiretional", config.bidirectional),
+            use_learnable_interaction=self.hparams.get(
+                "use_learnable_interactions", config.use_learnable_interaction
+            ),
         )
 
         norm_layer = self.hparams.get("norm", config.norm)

mambular/configs/fttransformer_config.py

@@ -27,4 +27,4 @@ class DefaultFTTransformerConfig:
     bias: bool = True
     transformer_activation: callable = nn.SELU()
     layer_norm_eps: float = 1e-05
-    transformer_dim_feedforward: int = 2048
+    transformer_dim_feedforward: int = 512

mambular/configs/mambular_config.py

@@ -32,3 +32,5 @@ class DefaultMambularConfig:
     head_use_batch_norm: bool = False
     layer_norm_after_embedding: bool = False
     pooling_method: str = "avg"
+    bidirectional: bool = False
+    use_learnable_interaction: bool = False

mambular/configs/tabtransformer_config.py

@@ -27,4 +27,4 @@ class DefaultTabTransformerConfig:
     bias: bool = True
     transformer_activation: callable = nn.SELU()
     layer_norm_eps: float = 1e-05
-    transformer_dim_feedforward: int = 2048
+    transformer_dim_feedforward: int = 512

mambular/models/sklearn_base_classifier.py

@@ -21,6 +21,8 @@ def __init__(self, model, config, **kwargs):
             "task",
             "cat_cutoff",
             "treat_all_integers_as_numerical",
+            "knots",
+            "degree",
         ]
 
         self.config_kwargs = {

mambular/models/sklearn_base_lss.py

@@ -43,6 +43,8 @@ def __init__(self, model, config, **kwargs):
             "task",
             "cat_cutoff",
             "treat_all_integers_as_numerical",
+            "knots",
+            "degree",
         ]
 
         self.config_kwargs = {

mambular/models/sklearn_base_regressor.py

@@ -20,6 +20,8 @@ def __init__(self, model, config, **kwargs):
             "task",
             "cat_cutoff",
             "treat_all_integers_as_numerical",
+            "knots",
+            "degree",
         ]
 
         self.config_kwargs = {