TF Model train and eval step metrics for seq2seq models. (#14009)

* TF Model train and eval step metrics for seq2seq models.

When using a model with a seq2seq output compute metrics against logits.

* Removing vestigial code
Co-authored-by: matt <rocketknight1@gmail.com>

src/transformers/modeling_tf_utils.py

@@ -43,6 +43,7 @@ from .file_utils import (
     is_remote_url,
 )
 from .generation_tf_utils import TFGenerationMixin
+from .modeling_tf_outputs import TFSeq2SeqLMOutput
 from .tokenization_utils_base import BatchEncoding
 from .utils import logging
 
@@ -787,6 +788,11 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
             loss = self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses)
         # Run backwards pass.
         self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
+        # When y_pred is a ModelOutput and y is a tf.Tensor the metrics update
+        # should be done only with the relevant ModelOutput param that is
+        # considered by the loss.
+        if isinstance(y_pred, TFSeq2SeqLMOutput) and isinstance(y, tf.Tensor):
+            y_pred = y_pred["logits"]
         self.compiled_metrics.update_state(y, y_pred, sample_weight)
         # Collect metrics to return
         return_metrics = {}
@@ -813,17 +819,13 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         if y is None and "labels" in x:
             y = x["labels"]  # Stops confusion with metric computations
         y_pred = self(x, training=False)
-        if not self.loss:
-            self.loss_tracker.update_state(y_pred.loss)
-            return_metrics = {"loss": self.loss_tracker.result()}
-        else:
-            # Run anyway to update state
         self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses)
-            return_metrics = {}
         # Updates stateful loss metrics.
-        self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses)
+        if isinstance(y_pred, TFSeq2SeqLMOutput) and isinstance(y, tf.Tensor):
+            y_pred = y_pred["logits"]
         self.compiled_metrics.update_state(y, y_pred, sample_weight)
         # Collect metrics to return
+        return_metrics = {}
         for metric in self.metrics:
             result = metric.result()
             if isinstance(result, dict):

tests/test_modeling_tf_t5.py

@@ -666,3 +666,33 @@ class TFT5ModelIntegrationTests(unittest.TestCase):
         translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
 
         self.assertEqual(translation, expected_translation)
+
+    def test_finetune_keras_trainer(self):
+        """Ensure that the model can be fine-tuned via the keras API and
+        that metrics work as expected.
+        """
+
+        # This metric expects to be called with the logits output
+        def _accuracy(y_true, y_pred):
+            return tf.keras.metrics.sparse_categorical_crossentropy(y_true[:, 0], y_pred[:, 0])
+
+        # measure the accuracy of the first token
+        class FirstTokenAccuracy(tf.keras.metrics.MeanMetricWrapper):
+            def __init__(self, name="accuracy", **kwargs):
+                super().__init__(_accuracy, name=name, **kwargs)
+
+        model = self.model
+        model.compile("adam", metrics=FirstTokenAccuracy())
+        tokenizer = T5Tokenizer.from_pretrained("t5-small")
+
+        examples = [
+            ("sentiment: Everything is awesome!", "positive"),
+            ("sentiment: Tensorflow datasets are hard to use", "negative"),
+        ]
+
+        inputs = dict(tokenizer([x[0] for x in examples], padding=True, return_tensors="tf"))
+        inputs["labels"] = tokenizer([x[1] for x in examples], return_tensors="tf").input_ids
+
+        model.fit(inputs)
+        m = model.evaluate(inputs)
+        self.assertEqual(len(m), 2)