BASIL-21-1: attention-vector

README.md

@@ -28,7 +28,7 @@ make dataset_advanced \
   RAW_DATASET=<path to raw json dialog file> \
   DIALOG_SECONDS_COOLDOWN=<minimum time distance in seconds between different dialogs> \
   DIALOG_MEMORY=<message number in memory to answer> \
-  DAATSET_OUTPUT=<path to save generated dataset folder>
+  DATASET_OUTPUT=<path to save generated dataset folder>
 ```
 
 ## Fit tokenizer

models/decoder.py

@@ -50,6 +50,7 @@ def __init__(
         self.embedding = nn.Embedding(
             num_embeddings=num_embeddings, embedding_dim=embedding_dim, max_norm=True
         )
+        self.combine_hiddens = nn.Linear(hidden_size * 2, hidden_size)
         self._dec_lstm = nn.LSTM(
             input_size=embedding_dim,
             hidden_size=hidden_size,
@@ -74,8 +75,12 @@ def forward(self, tokens, context, mask=None, *args, **kwargs):
         dec_in_h = context["hidden"]
         dec_in_c = context["context"]
         encoder_outputs = context["encoder_output"]
+        attention_context = context.get("attention_context", None)
 
         embeddings = self.embedding(tokens)
+        if attention_context is not None:
+            attention_context = attention_context.transpose(0, 1).repeat(dec_in_h.shape[0], 1, 1).contiguous()
+            dec_in_h = self.combine_hiddens(torch.cat([attention_context, dec_in_h], dim=-1))
         dec_out, (dec_h, dec_c) = self._dec_lstm(embeddings, (dec_in_h, dec_in_c))
 
         attention_context, attention_weights = self._attention(
@@ -95,5 +100,6 @@ def forward(self, tokens, context, mask=None, *args, **kwargs):
                 "context": dec_c,
                 "encoder_output": encoder_outputs,
                 "attention": attention_weights,
+                "attention_context": attention_context,
             },
         )

pl/modules.py

@@ -1,8 +1,8 @@
 from typing import Any, Dict, Optional, Sequence, Union
 
+import os
 import lightning as pl
 import torch
-import torchtext
 import torchvision
 
 from tokenizers import BaseTokenizer
@@ -37,6 +37,14 @@ def __init__(self, config: Dict[str, Any]) -> None:
             start_token=self.tokenizer.start_token,
             stop_token=self.tokenizer.stop_token,
         )
+        checkpoint_path = config.get("checkpoint", None)
+        if checkpoint_path is not None and os.path.isfile(checkpoint_path):
+            checkpoint = torch.load(checkpoint_path, map_location="cpu")
+            model_state_dict = {
+                key.replace("model.", ""): val
+                for key, val in checkpoint["state_dict"].items() if "model." in key
+            }
+            self.model.load_state_dict(model_state_dict)
         self._log_attention = self.model_config['parameters'].get('use_attention', False)
         self.train_batch_size: Optional[int] = self.dataset_config.get(
             "train_batch", None
@@ -99,7 +107,6 @@ def __step(self, batch: Dict[str, Any], batch_idx: int, stage: str) -> torch.Ten
             {
                 f"Loss/{stage}": loss,
                 f"Accuracy/{stage}": accuracy,
-                f"Resources/RAM": get_ram_consumption_mb(),
                 f"BLEU/{stage}": bleu,
                 f"Resources/RAM": get_ram_consumption_mb(),
             },
@@ -141,8 +148,8 @@ def validation_step(self, batch: Dict[str, Any], batch_idx: int) -> None:
                     )
 
                 val_result = ""
-                for sample_idx in range(max(len(in_tokens), 16)):
-                    prediction = self.tokenizer.decode(tokens[sample_idx].tolist()).replace("PAD", " ")
+                for sample_idx in range(min(len(in_tokens), 16)):
+                    prediction = self.tokenizer.decode(tokens[sample_idx].tolist()).split("STOP", 1)[0].replace("PAD", "").replace("STOP", "")
                     val_result += (
                         f"Sample #{sample_idx}:\n\ninput: {text_input[sample_idx]}\n\npredicted: "
                         f"{prediction}\n\ntarget: {target_output[sample_idx]}\n\n"

utils/visualization.py

@@ -26,9 +26,10 @@ def plot_attention_scores(input_words, output_words, scores, figsize=(4, 4), dpi
 
     plt.tight_layout(pad=0.5, w_pad=0.5, h_pad=1.0)
 
-    image = None
     with tempfile.NamedTemporaryFile(suffix='.png', delete=True) as temp_file:
         fig.savefig(temp_file.name)
-        image = plt.imread(temp_file.name)[:,:,:3]
+        image = plt.imread(temp_file.name)[:, :, :3]
+
+    plt.close(fig)
 
     return image