Add mamba specific generator

language_interpolation/utils.py

@@ -158,6 +158,63 @@ def justify_sample(sample):
 
     return results
 
+def generate_mamba_text(
+    model: nn.Module,
+    characters_per_feature: int,
+    max_characters: int,
+    text_list: List[str],
+    output_size: int,
+    topk: int = 1,
+):
+    """
+    TODO: This can be done much more efficiently because right now I'm re-computing
+    the output from the 0th input every time, while those may be identical (I think)
+    and so can be re-used. I think I have quadratic generation here when it should
+    be linear.
+
+    :param characters_per_feature: typically 1, the number of characters that make up
+    a feature.
+    :param max_characters: The maximum number of characters to use, acts like a moving
+    window. Set to 0 if all characters should be used.
+    :param text_list: List of prompts
+    :param output_size: The number of characters to generate
+    :param topk: weighted random selection of the topk next characters
+    :returns: the continuation of the prompts, the original text + the next output_size
+    characters
+    """
+    model.eval()
+
+
+    results = []
+    for text_raw in text_list:
+        text_in = text_raw
+        for i in range(output_size):
+            encoding, text_used = encode_input_from_text(
+                text_in=text_in, features=max_characters
+            )
+            encoding = (
+                encoding
+                .to(model._device)
+                .reshape(1, -1, characters_per_feature)
+            )
+            model.eval()
+
+            output = model(encoding)
+            values, indices, ascii = decode_output_to_text(
+                encoding=output[0,-1,:], topk=topk
+            )
+
+            # pick the next character weighted by probabilities of each character
+            # prevents the same response for every query.
+            values = values.nan_to_num(nan=1.0)
+            actual = random.choices(ascii, values.tolist())
+            text_in = text_in + actual[0]
+
+        results.append(text_in.replace("\n", " "))
+
+    return results
+
+
 
 class TextGenerationSampler(Callback):
     def __init__(self, cfg):
@@ -180,6 +237,16 @@ def on_train_epoch_end(self, trainer, pl_module, outputs=None):
                         output_size=self._cfg.num_predict,
                         topk=topk,
                     )
+                elif self._cfg.net.model_type in ["mamba"]:
+                    predictions = generate_mamba_text(
+                        pl_module,
+                        characters_per_feature=self._cfg.data.characters_per_feature,
+                        max_characters=self._cfg.data.characters_per_feature
+                        * self._cfg.data.max_features,
+                        text_list=self._cfg.prompts,
+                        output_size=self._cfg.num_predict,
+                        topk=topk,
+                    )
                 else:
                     predictions = generate_text(
                         pl_module,