efficient GMM attneiton with native broadcasting

layers/common_layers.py

@@ -82,6 +82,11 @@ class Prenet(nn.Module):
         return x
 
 
+####################
+# ATTENTION MODULES
+####################
+
+
 class LocationLayer(nn.Module):
     def __init__(self,
                  attention_dim,
@@ -105,87 +110,6 @@ class LocationLayer(nn.Module):
         return processed_attention
 
 
-class GravesAttention(nn.Module):
-    """ Graves attention as described here:
-        - https://arxiv.org/abs/1910.10288
-    """
-    COEF = 0.3989422917366028  # numpy.sqrt(1/(2*numpy.pi))
-
-    def __init__(self, query_dim, K):
-        super(GravesAttention, self).__init__()
-        self._mask_value = 0.0
-        self.K = K
-        # self.attention_alignment = 0.05
-        self.eps = 1e-5
-        self.J = None
-        self.N_a = nn.Sequential(
-            nn.Linear(query_dim, query_dim, bias=True),
-            nn.ReLU(),
-            nn.Linear(query_dim, 3*K, bias=True))
-        self.attention_weights = None
-        self.mu_prev = None
-        self.init_layers()
-
-    def init_layers(self):
-        torch.nn.init.constant_(self.N_a[2].bias[(2*self.K):(3*self.K)], 1.)
-        torch.nn.init.constant_(self.N_a[2].bias[self.K:(2*self.K)], 10)
-
-    def init_states(self, inputs):
-        if self.J is None or inputs.shape[1] > self.J.shape[-1]:
-            self.J = torch.arange(0, inputs.shape[1]).to(inputs.device).expand([inputs.shape[0], self.K, inputs.shape[1]])
-        self.attention_weights = torch.zeros(inputs.shape[0], inputs.shape[1]).to(inputs.device)
-        self.mu_prev = torch.zeros(inputs.shape[0], self.K).to(inputs.device)
-
-    # pylint: disable=R0201
-    # pylint: disable=unused-argument
-    def preprocess_inputs(self, inputs):
-        return None
-
-    def forward(self, query, inputs, processed_inputs, mask):
-        """
-        shapes:
-            query: B x D_attention_rnn
-            inputs: B x T_in x D_encoder
-            processed_inputs: place_holder
-            mask: B x T_in
-        """
-        gbk_t = self.N_a(query)
-        gbk_t = gbk_t.view(gbk_t.size(0), -1, self.K)
-
-        # attention model parameters
-        # each B x K
-        g_t = gbk_t[:, 0, :]
-        b_t = gbk_t[:, 1, :]
-        k_t = gbk_t[:, 2, :]
-
-        # attention GMM parameters
-        sig_t = torch.nn.functional.softplus(b_t) + self.eps
-
-        mu_t = self.mu_prev + torch.nn.functional.softplus(k_t)
-        g_t = torch.softmax(g_t, dim=-1) / sig_t + self.eps
-
-        # each B x K x T_in
-        g_t = g_t.unsqueeze(2).expand(g_t.size(0),
-                                      g_t.size(1),
-                                      inputs.size(1))
-        sig_t = sig_t.unsqueeze(2).expand_as(g_t)
-        mu_t_ = mu_t.unsqueeze(2).expand_as(g_t)
-        j = self.J[:g_t.size(0), :, :inputs.size(1)]
-
-        # attention weights
-        phi_t = g_t * torch.exp(-0.5 * (mu_t_ - j)**2 / (sig_t**2))
-        alpha_t = self.COEF * torch.sum(phi_t, 1)
-
-        # apply masking
-        if mask is not None:
-            alpha_t.data.masked_fill_(~mask, self._mask_value)
-
-        context = torch.bmm(alpha_t.unsqueeze(1), inputs).squeeze(1)
-        self.attention_weights = alpha_t
-        self.mu_prev = mu_t
-        return context
-
-
 class OriginalAttention(nn.Module):
     """Following the methods proposed here:
         - https://arxiv.org/abs/1712.05884
@@ -365,6 +289,82 @@ class OriginalAttention(nn.Module):
         return context
 
 
+class GravesAttention(nn.Module):
+    """ Graves attention as described here:
+        - https://arxiv.org/abs/1910.10288
+    """
+    COEF = 0.3989422917366028  # numpy.sqrt(1/(2*numpy.pi))
+
+    def __init__(self, query_dim, K):
+        super(GravesAttention, self).__init__()
+        self._mask_value = 0.0
+        self.K = K
+        # self.attention_alignment = 0.05
+        self.eps = 1e-5
+        self.J = None
+        self.N_a = nn.Sequential(
+            nn.Linear(query_dim, query_dim, bias=True),
+            nn.ReLU(),
+            nn.Linear(query_dim, 3*K, bias=True))
+        self.attention_weights = None
+        self.mu_prev = None
+        self.init_layers()
+
+    def init_layers(self):
+        torch.nn.init.constant_(self.N_a[2].bias[(2*self.K):(3*self.K)], 1.)
+        torch.nn.init.constant_(self.N_a[2].bias[self.K:(2*self.K)], 10)
+
+    def init_states(self, inputs):
+        if self.J is None or inputs.shape[1] > self.J.shape[-1]:
+            self.J = torch.arange(0, inputs.shape[1]).to(inputs.device)
+        self.attention_weights = torch.zeros(inputs.shape[0], inputs.shape[1]).to(inputs.device)
+        self.mu_prev = torch.zeros(inputs.shape[0], self.K).to(inputs.device)
+
+    # pylint: disable=R0201
+    # pylint: disable=unused-argument
+    def preprocess_inputs(self, inputs):
+        return None
+
+    def forward(self, query, inputs, processed_inputs, mask):
+        """
+        shapes:
+            query: B x D_attention_rnn
+            inputs: B x T_in x D_encoder
+            processed_inputs: place_holder
+            mask: B x T_in
+        """
+        gbk_t = self.N_a(query)
+        gbk_t = gbk_t.view(gbk_t.size(0), -1, self.K)
+
+        # attention model parameters
+        # each B x K
+        g_t = gbk_t[:, 0, :]
+        b_t = gbk_t[:, 1, :]
+        k_t = gbk_t[:, 2, :]
+
+        # attention GMM parameters
+        sig_t = torch.nn.functional.softplus(b_t) + self.eps
+
+        mu_t = self.mu_prev + torch.nn.functional.softplus(k_t)
+        g_t = torch.softmax(g_t, dim=-1) / sig_t + self.eps
+
+        # each B x K x T_in
+        j = self.J[:inputs.size(1)]
+
+        # attention weights
+        phi_t = g_t.unsqueeze(-1) * torch.exp(-0.5 * (mu_t.unsqueeze(-1) - j)**2 / (sig_t.unsqueeze(-1)**2))
+        alpha_t = self.COEF * torch.sum(phi_t, 1)
+
+        # apply masking
+        if mask is not None:
+            alpha_t.data.masked_fill_(~mask, self._mask_value)
+
+        context = torch.bmm(alpha_t.unsqueeze(1), inputs).squeeze(1)
+        self.attention_weights = alpha_t
+        self.mu_prev = mu_t
+        return context
+
+
 def init_attn(attn_type, query_dim, embedding_dim, attention_dim,
               location_attention, attention_location_n_filters,
               attention_location_kernel_size, windowing, norm, forward_attn,