Add knowledge-base lookup to model

egs/librispeech/ASR/pruned2_knowledge/conformer.py

@@ -19,6 +19,7 @@ import copy
 import math
 import warnings
 from typing import Optional, Tuple
+from sampling import create_knowledge_base, KnowledgeBaseLookup
 
 import torch
 from encoder_interface import EncoderInterface
@@ -61,6 +62,10 @@ class Conformer(EncoderInterface):
         dropout: float = 0.1,
         layer_dropout: float = 0.075,
         cnn_module_kernel: int = 31,
+        knowledge_M: int = 256,
+        knowledge_N: int = 2,
+        knowledge_D: int = 256,
+        knowledge_K: int = 16,
     ) -> None:
         super(Conformer, self).__init__()
 
@@ -69,6 +74,10 @@ class Conformer(EncoderInterface):
         if subsampling_factor != 4:
             raise NotImplementedError("Support only 'subsampling_factor=4'.")
 
+
+        self.knowledge_base = create_knowledge_base(knowledge_M, knowledge_N,
+                                                    knowledge_D)
+
         # self.encoder_embed converts the input of shape (N, T, num_features)
         # to the shape (N, T//subsampling_factor, d_model).
         # That is, it does two things simultaneously:
@@ -79,12 +88,17 @@ class Conformer(EncoderInterface):
         self.encoder_pos = RelPositionalEncoding(d_model, dropout)
 
         encoder_layer = ConformerEncoderLayer(
+            self.knowledge_base,
             d_model,
             nhead,
             dim_feedforward,
             dropout,
             layer_dropout,
             cnn_module_kernel,
+            knowledge_M,
+            knowledge_N,
+            knowledge_D,
+            knowledge_K
         )
         self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers)
 
@@ -134,11 +148,15 @@ class ConformerEncoderLayer(nn.Module):
     See: "Conformer: Convolution-augmented Transformer for Speech Recognition"
 
     Args:
+      knowledge_base: shared knowledge base parameter matrix, to be passed to constructors
+              of lookup modules
         d_model: the number of expected features in the input (required).
         nhead: the number of heads in the multiheadattention models (required).
         dim_feedforward: the dimension of the feedforward network model (default=2048).
         dropout: the dropout value (default=0.1).
         cnn_module_kernel (int): Kernel size of convolution module.
+     knowledge_M, knowledge_N, knowledge_D, knowledge_K: parameters for knowledge-base,
+       see docs for KnowlegeBaseLookup.
 
     Examples::
         >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8)
@@ -149,12 +167,17 @@ class ConformerEncoderLayer(nn.Module):
 
     def __init__(
         self,
+        knowledge_base: nn.Parameter,
         d_model: int,
         nhead: int,
         dim_feedforward: int = 2048,
         dropout: float = 0.1,
         layer_dropout: float = 0.075,
         cnn_module_kernel: int = 31,
+        knowledge_M: int = 256,
+        knowledge_N: int = 2,
+        knowledge_D: int = 256,
+        knowledge_K: int = 16,
     ) -> None:
         super(ConformerEncoderLayer, self).__init__()
 
@@ -184,6 +207,11 @@ class ConformerEncoderLayer(nn.Module):
 
         self.conv_module = ConvolutionModule(d_model, cnn_module_kernel)
 
+        self.lookup = KnowledgeBaseLookup(knowledge_M, knowledge_N,
+                                          knowledge_D, knowledge_K,
+                                          d_model,
+                                          knowledge_base)
+
         self.norm_final = BasicNorm(d_model)
 
         # try to ensure the output is close to zero-mean (or at least, zero-median).
@@ -253,6 +281,9 @@ class ConformerEncoderLayer(nn.Module):
         # feed forward module
         src = src + self.dropout(self.feed_forward(src))
 
+        # knowledge-base lookup
+        src = src + self.dropout(self.lookup(src))
+
         src = self.norm_final(self.balancer(src))
 
         if alpha != 1.0:

egs/librispeech/ASR/pruned2_knowledge/sampling.py

@@ -417,9 +417,12 @@ def get_indexes_for_samples(P: Tensor,
         # right=True means we find
         # P_cumsum[...,index-1] <= this_samples[...,k] < P_cumsum[...,index],
         # which is what we want, as opposed to ... < ... <= (i.e. swap < and <=)
-        idx = ans_indexes[...,n] = torch.searchsorted(P_cumsum[...,n,:], # (*, M)
-                                                      this_samples, # (*, K)
-                                                      right=True)
+        # .contiguous() suppresses a warning about searchsorted needing contiguous
+        # input.  N tends to be 2 or 3 so this copy is not too big a deal.
+        idx = ans_indexes[...,n] = torch.searchsorted(
+            P_cumsum[...,n,:].contiguous(), # (*, M)
+            this_samples, # (*, K)
+            right=True)
         this_P = torch.gather(P[...,n,:], dim=-1, index=idx)  # shape: (*, K)
 
         if n == 0: