docstring, xattn working

seq2seq_time/models/transformer_process.py

@@ -121,8 +121,12 @@ class Decoder(nn.Module):
         return dist
         
 class TransformerProcess(nn.Module):
-    # WIP trying one that encodes a dist
-    # TODO autoregressive mask
+    """
+    A attempted simplification of an attentive neural process
+
+    Works on sequential data, has no deterministic encoder. Uses full transformer layer instead of custom attention. Has an autoregressive mask on the encoder and decoder.
+
+    """
     def __init__(self, x_size, y_size, hidden_size=64, latent_dim=32, nhead=8, nlayers=4, dropout=0, min_std=0.01):
         super().__init__()
         self._min_std = min_std

seq2seq_time/models/xattention.py

@@ -6,7 +6,7 @@ from ..util import mask_upper_triangular
 
 class CrossAttention(nn.Module):
     """
-    A single transformer, masking nan or 0
+    A single transformer,  using cross attention, like in the determistic encoder in attentive neural processes.
     """
     def __init__(self, x_dim, y_dim, attention_dropout=0, nhead=8, nlayers=8, hidden_size=32, nan_value=0, min_std=0.01):
         super().__init__()
@@ -14,7 +14,22 @@ class CrossAttention(nn.Module):
         self.nan_value = nan_value
         enc_x_dim = x_dim + y_dim
 
-        self.enc_emb = nn.Linear(enc_x_dim, hidden_size)
+        self.v_encoder = nn.Linear(enc_x_dim, hidden_size)
+        self.k_encoder = nn.Linear(x_dim, hidden_size)
+        self.q_encoder = nn.Linear(x_dim, hidden_size)
+        self.self_attn_k = torch.nn.MultiheadAttention(
+            hidden_size, nhead, bias=False, dropout=attention_dropout
+        )
+        self.self_attn_q = torch.nn.MultiheadAttention(
+            hidden_size, nhead, bias=False, dropout=attention_dropout
+        )
+        self.self_attn_v = torch.nn.MultiheadAttention(
+            hidden_size, nhead, bias=False, dropout=attention_dropout
+        )
+        self.cross_attn = torch.nn.MultiheadAttention(
+            hidden_size, nhead, bias=False, dropout=attention_dropout
+        )
+
         encoder_norm = nn.LayerNorm(hidden_size)
         layer_enc = nn.TransformerEncoderLayer(
             d_model=hidden_size,
@@ -23,7 +38,7 @@ class CrossAttention(nn.Module):
             nhead=nhead,
             # activation
         )
-        self.encoder = nn.TransformerEncoder(
+        self.transformer = nn.TransformerEncoder(
             layer_enc, num_layers=nlayers, norm=encoder_norm
         )
         self.mean = nn.Linear(hidden_size, y_dim)
@@ -31,35 +46,34 @@ class CrossAttention(nn.Module):
 
     def forward(self, past_x, past_y, future_x, future_y=None):
         device = next(self.parameters()).device
-        B, S, _ = future_x.shape
-        future_y_fake = past_y[:, -1:, :].repeat(1, S, 1).to(device)
-        # future_y_fake = (
-        #     torch.ones(past_y.shape[0], future_x.shape[1], past_y.shape[2]).float().to(device) * past_y[:, -1].repeat(B, S, 1)
-        # )
         context = torch.cat([past_x, past_y], -1).detach()
-        target = torch.cat([future_x, future_y_fake], -1).detach()
-        x = torch.cat([context, target * 1], 1).detach()
 
         # Masks
-        x_mask = torch.isfinite(x) & (x != self.nan_value)
-        x[~x_mask] = 0
-        x = x.detach()
-        x_key_padding_mask = ~x_mask.any(-1)
+        B, C, _ = past_x.shape
+        past_causal_mask = mask_upper_triangular(C, device)
+        B, T, _ = future_x.shape
+        future_causal_mask = mask_upper_triangular(T, device)
 
-        x = self.enc_emb(x).permute(1, 0, 2)
+        # Change feature size
+        k = self.k_encoder(past_x).permute(1, 0, 2)
+        q = self.q_encoder(future_x).permute(1, 0, 2)
+        v = self.v_encoder(context).permute(1, 0, 2)
 
-        S, B, _ = x.shape
-        mask = mask_upper_triangular(S, device)
-        
-        outputs = self.encoder(x, mask=mask#, src_key_padding_mask=x_key_padding_mask
-        ).permute(
-            1, 0, 2
-        )
+        # Self attention with causal mask
+        v = self.self_attn_v(v, v, v, attn_mask=past_causal_mask)[0]
+        q = self.self_attn_q(q, q, q, attn_mask=future_causal_mask)[0]
+        k = self.self_attn_k(k, k, k, attn_mask=past_causal_mask)[0]
 
-        # Seems to help a little, especially with extrapolating out of bounds
-        steps = past_y.shape[1]
-        mean = self.mean(outputs)[:, steps:, :]
-        log_sigma = self.std(outputs)[:, steps:, :]
+        # Cross attention
+        h = self.cross_attn(query=q, key=k, value=v)[0]
+
+        # Transformer
+        outputs = self.transformer(h, mask=future_causal_mask)
+        outputs = outputs.permute(1, 0, 2)
+
+        # Head
+        mean = self.mean(outputs)
+        log_sigma = self.std(outputs)
         
         sigma = self._min_std + (1 - self._min_std) * F.softplus(log_sigma)
         return torch.distributions.Normal(mean, sigma), {}

seq2seq_time/silence.py

@@ -0,0 +1,3 @@
+import warnings
+warnings.filterwarnings('ignore', 'Consider increasing the value of the `num_workers` argument', UserWarning)
+warnings.filterwarnings('ignore', 'Your val_dataloader has `shuffle=True`', UserWarning)