Fix KerasMetricCallback prediction with generate() and inference of c…

…olumn names (#15351)

* Fix prediction with generate() and the inference of column names
Should now have very few differences with the PyTorch implementation

* Minor edit to parent class

* Update src/transformers/keras_callbacks.py

Co-authored-by: Sylvain Gugger <[email protected]>

* Explaining the dict conversion

* Putting main_input_name back

* Fixes to main_input_name

Co-authored-by: Sylvain Gugger <[email protected]>

src/transformers/keras_callbacks.py

@@ -56,8 +56,6 @@ def rouge_fn(predictions, labels):
             metric names to numerical values.
         eval_dataset (`tf.data.Dataset` or `dict` or `tuple` or `np.ndarray` or `tf.Tensor`):
             Validation data to be used to generate predictions for the `metric_fn`.
-        metric_fn_kwargs (`dict`, *optional*):
-            Additional keyword arguments to be passed to the metric_fn.
         output_cols (`List[str], *optional*):
             A list of columns to be retained from the model output as the predictions. Defaults to all.
         label_cols ('`List[str]`, *optional*'):
@@ -74,7 +72,6 @@ def __init__(
         self,
         metric_fn: Callable,
         eval_dataset: Union[tf.data.Dataset, np.ndarray, tf.Tensor, tuple, dict],
-        metric_fn_kwargs: Optional[dict] = None,
         output_cols: Optional[List[str]] = None,
         label_cols: Optional[List[str]] = None,
         batch_size: Optional[int] = None,
@@ -94,12 +91,6 @@ def __init__(
         self.eval_dataset = eval_dataset
         self.predict_with_generate = predict_with_generate
         self.output_cols = output_cols
-        self.metric_fn_kwargs = metric_fn_kwargs or dict()
-
-        if hasattr(self.model, "encoder") and self.model.encoder.main_input_name != self.model.main_input_name:
-            self.main_input_name = self.model.encoder.main_input_name
-        else:
-            self.main_input_name = self.model.main_input_name
 
         # This next block attempts to parse out which elements of the dataset should be appended to the labels list
         # that is passed to the metric_fn
@@ -123,32 +114,75 @@ def __init__(
             self.label_cols = ["labels"]
             self.use_keras_label = False
             logging.warning("No label_cols specified for KerasMetricCallback, assuming you want the 'labels' key.")
+        elif "start_positions" in input_spec and "end_positions" in input_spec:
+            self.label_cols = ["start_positions", "end_positions"]
+            self.use_keras_label = False
+            logging.warning(
+                "No label_cols specified for KerasMetricCallback, assuming you want the "
+                "start_positions and end_positions keys."
+            )
         else:
             raise ValueError("Could not autodetect label_cols for KerasMetricCallback, please specify them!")
-        if parse(tf.__version__).minor < parse("2.7"):
+        if parse(tf.__version__) < parse("2.7"):
             logging.warning("TF versions less than 2.7 may encounter issues with KerasMetricCallback!")
 
     @staticmethod
-    def _concatenate_batches(batches):
-        # Flattens Numpy array batches into a list of single samples, where each sample is still np.ndarray
-        return [sample for batch in batches for sample in batch]
+    def _concatenate_batches(batches, padding_index=-100):
+        # If all batches are unidimensional or same length, do a simple concatenation
+        if batches[0].ndim == 1 or all([batch.shape[1] == batches[0].shape[1] for batch in batches]):
+            return np.concatenate(batches, axis=0)
+
+        # Welp, they're not the same length. Let's do some padding
+        max_len = max([batch.shape[1] for batch in batches])
+        num_samples = sum([batch.shape[0] for batch in batches])
+        output = np.full_like(
+            batches[0], fill_value=padding_index, shape=[num_samples, max_len] + list(batches[0].shape[2:])
+        )
+        # i keeps track of which part of the concatenated array we're writing the next batch to
+        i = 0
+        for batch in batches:
+            output[i : i + len(batch), : batch.shape[1]] = batch
+            i += len(batch)
+        return output
 
     def _postprocess_predictions_or_labels(self, inputs):
         if isinstance(inputs[0], dict):
             outputs = dict()
             for key in inputs[0].keys():
-                outputs[key] = self._concatenate_batches(batch[key] for batch in inputs)
+                outputs[key] = self._concatenate_batches([batch[key] for batch in inputs])
+            # If it's a dict with only one key, just return the array
+            if len(outputs) == 1:
+                outputs = list(outputs.values())[0]
         elif isinstance(inputs[0], list) or isinstance(inputs[0], tuple):
             outputs = []
             for input_list in zip(*inputs):
                 outputs.append(self._concatenate_batches(input_list))
+            if len(outputs) == 1:
+                outputs = outputs[0]  # If it's a list with only one element, just return the array
         elif isinstance(inputs[0], np.ndarray):
             outputs = self._concatenate_batches(inputs)
+        elif isinstance(inputs[0], tf.Tensor):
+            outputs = self._concatenate_batches([tensor.numpy() for tensor in inputs])
         else:
             raise TypeError(f"Couldn't handle batch of type {type(inputs[0])}!")
         return outputs
 
     def on_epoch_end(self, epoch, logs=None):
+        if hasattr(self.model, "config"):
+            ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", [])
+        else:
+            ignore_keys = []
+
+        main_input_name = None
+        if self.predict_with_generate:
+            # This dense conditional recognizes the case where we have an encoder-decoder model, but
+            # avoids getting tangled up when we just have a model with a layer called 'encoder'
+            if hasattr(self.model, "encoder") and hasattr(self.model.encoder, "main_input_name"):
+                if self.model.encoder.main_input_name != self.model.main_input_name:
+                    main_input_name = self.model.encoder.main_input_name
+            else:
+                main_input_name = getattr(self.model, "main_input_name", "input_ids")
+
         prediction_list = []
         label_list = []
 
@@ -160,7 +194,7 @@ def on_epoch_end(self, epoch, logs=None):
                 labels = None
             if self.predict_with_generate:
                 if isinstance(batch, dict):
-                    generation_inputs = batch[self.main_input_name]
+                    generation_inputs = batch[main_input_name]
                     attention_mask = batch.get("attention_mask", None)
                 else:
                     generation_inputs = batch
@@ -169,9 +203,14 @@ def on_epoch_end(self, epoch, logs=None):
                 predictions = self.model.generate(generation_inputs, attention_mask=attention_mask)
             else:
                 predictions = self.model.predict(batch)
-            predictions = dict(predictions)
-            if self.output_cols is not None:
-                predictions = {key: predictions[key] for key in self.output_cols}
+                if isinstance(predictions, dict):
+                    # This converts any dict-subclass to a regular dict
+                    # Keras REALLY doesn't like it when we pass around a BatchEncoding or other derived class
+                    predictions = dict(predictions)
+                if self.output_cols is not None:
+                    predictions = {key: predictions[key] for key in self.output_cols}
+                else:
+                    predictions = {key: val for key, val in predictions.items() if key not in ignore_keys + ["loss"]}
             prediction_list.append(predictions)
             if not self.use_keras_label:
                 labels = {key: batch[key].numpy() for key in self.label_cols}
@@ -185,10 +224,10 @@ def on_epoch_end(self, epoch, logs=None):
                 raise TypeError(f"Confused by labels of type {type(labels)}")
             label_list.append(labels)
 
-        prediction_list = self._postprocess_predictions_or_labels(prediction_list)
-        label_list = self._postprocess_predictions_or_labels(label_list)
+        all_preds = self._postprocess_predictions_or_labels(prediction_list)
+        all_labels = self._postprocess_predictions_or_labels(label_list)
 
-        metric_output = self.metric_fn(prediction_list, label_list, **self.metric_fn_kwargs)
+        metric_output = self.metric_fn((all_preds, all_labels))
         if not isinstance(metric_output, dict):
             raise TypeError(
                 f"metric_fn should return a dict mapping metric names to values but instead returned {metric_output}"