Generate: Export TF generate with a TF tokenizer (#22310)

* Export TF generate with a TF tokenizer

* remove unused lines

src/transformers/generation/tf_utils.py

@@ -1725,14 +1725,13 @@ class TFGenerationMixin:
 
         # 2-to-n generation steps can then be run in autoregressive fashion
         # only in case 1st generation step does NOT yield EOS token though
-        if greedy_search_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
-            maximum_iterations = max_length - cur_len
-            generated, _, cur_len, _ = tf.while_loop(
-                greedy_search_cond_fn,
-                greedy_search_body_fn,
-                (generated, finished_sequences, cur_len, model_kwargs),
-                maximum_iterations=maximum_iterations,
-            )
+        maximum_iterations = max_length - cur_len
+        generated, _, cur_len, _ = tf.while_loop(
+            greedy_search_cond_fn,
+            greedy_search_body_fn,
+            (generated, finished_sequences, cur_len, model_kwargs),
+            maximum_iterations=maximum_iterations,
+        )
 
         # 6. prepare outputs
         if not use_xla:
@@ -2016,14 +2015,13 @@ class TFGenerationMixin:
 
         # 2-to-n generation steps can then be run in autoregressive fashion
         # only in case 1st generation step does NOT yield EOS token though
-        if sample_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
-            maximum_iterations = max_length - cur_len
-            generated, _, cur_len, _ = tf.while_loop(
-                sample_cond_fn,
-                sample_body_fn,
-                (generated, finished_sequences, cur_len, model_kwargs),
-                maximum_iterations=maximum_iterations,
-            )
+        maximum_iterations = max_length - cur_len
+        generated, _, cur_len, _ = tf.while_loop(
+            sample_cond_fn,
+            sample_body_fn,
+            (generated, finished_sequences, cur_len, model_kwargs),
+            maximum_iterations=maximum_iterations,
+        )
 
         # 6. prepare outputs
         if not use_xla:
@@ -2565,7 +2563,8 @@ class TFGenerationMixin:
 
         # 2-to-n generation steps can then be run in autoregressive fashion (only in case 1st generation step does
         # NOT yield EOS token though)
-        if beam_search_cond_fn(
+        maximum_iterations = max_length - cur_len
+        (
             cur_len,
             running_sequences,
             running_scores,
@@ -2574,9 +2573,10 @@ class TFGenerationMixin:
             scores,
             beam_indices,
             is_sent_finished,
-            model_kwargs,
-        ):
-            maximum_iterations = max_length - cur_len
+            _,
+        ) = tf.while_loop(
+            beam_search_cond_fn,
+            beam_search_body_fn,
             (
                 cur_len,
                 running_sequences,
@@ -2586,23 +2586,10 @@ class TFGenerationMixin:
                 scores,
                 beam_indices,
                 is_sent_finished,
-                _,
-            ) = tf.while_loop(
-                beam_search_cond_fn,
-                beam_search_body_fn,
-                (
-                    cur_len,
-                    running_sequences,
-                    running_scores,
-                    running_beam_indices,
-                    sequences,
-                    scores,
-                    beam_indices,
-                    is_sent_finished,
-                    model_kwargs,
-                ),
-                maximum_iterations=maximum_iterations,
-            )
+                model_kwargs,
+            ),
+            maximum_iterations=maximum_iterations,
+        )
 
         # 6. prepare outputs
         # Account for the edge-case where there are no finished sequences for a particular batch item. If so, return
@@ -3019,22 +3006,13 @@ class TFGenerationMixin:
 
         # 2-to-n generation steps can then be run in autoregressive fashion
         # only in case 1st generation step does NOT yield EOS token though
-        if contrastive_search_cond_fn(
-            generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
-        ):
-            maximum_iterations = max_length - cur_len
-            (
-                generated,
-                _,
-                cur_len,
-                _,
-                _,
-            ) = tf.while_loop(
-                contrastive_search_cond_fn,
-                contrastive_search_body_fn,
-                (generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables),
-                maximum_iterations=maximum_iterations,
-            )
+        maximum_iterations = max_length - cur_len
+        generated, _, cur_len, _, _ = tf.while_loop(
+            contrastive_search_cond_fn,
+            contrastive_search_body_fn,
+            (generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables),
+            maximum_iterations=maximum_iterations,
+        )
 
         # 6. prepare outputs
         if not use_xla:

tests/generation/test_tf_utils.py

@@ -13,13 +13,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import tempfile
 import unittest
 
 import numpy as np
+from huggingface_hub import hf_hub_download
 
-from transformers import is_tf_available
-from transformers.testing_utils import require_tf, slow
+from transformers import is_tensorflow_text_available, is_tf_available
+from transformers.testing_utils import require_tensorflow_text, require_tf, slow
 
 from ..test_modeling_tf_common import floats_tensor
 from .test_framework_agnostic import GenerationIntegrationTestsMixin
@@ -40,6 +42,9 @@ if is_tf_available():
         tf_top_k_top_p_filtering,
     )
 
+if is_tensorflow_text_available():
+    import tensorflow_text as text
+
 
 @require_tf
 class UtilsFunctionsTest(unittest.TestCase):
@@ -239,6 +244,36 @@ class TFGenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTests
                 tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_new_tokens)
                 tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs)
 
+    @slow
+    @require_tensorflow_text
+    def test_generate_tf_function_export_with_tf_tokenizer(self):
+        # TF-only test: tf.saved_model export
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            # file needed to load the TF tokenizer
+            hf_hub_download(repo_id="google/flan-t5-small", filename="spiece.model", local_dir=tmp_dir)
+
+            class CompleteSentenceTransformer(tf.keras.layers.Layer):
+                def __init__(self):
+                    super().__init__()
+                    self.tokenizer = text.SentencepieceTokenizer(
+                        model=tf.io.gfile.GFile(os.path.join(tmp_dir, "spiece.model"), "rb").read()
+                    )
+                    self.model = TFAutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+                def call(self, inputs, *args, **kwargs):
+                    tokens = self.tokenizer.tokenize(inputs)
+                    input_ids, attention_mask = text.pad_model_inputs(
+                        tokens, max_seq_length=64, pad_value=self.model.config.pad_token_id
+                    )
+                    outputs = self.model.generate(input_ids=input_ids, attention_mask=attention_mask)
+                    return self.tokenizer.detokenize(outputs)
+
+            complete_model = CompleteSentenceTransformer()
+            inputs = tf.keras.layers.Input(shape=(1,), dtype=tf.string, name="inputs")
+            outputs = complete_model(inputs)
+            keras_model = tf.keras.Model(inputs, outputs)
+            keras_model.save(tmp_dir)
+
     def test_eos_token_id_int_and_list_top_k_top_sampling(self):
         # Has PT equivalent: this test relies on random sampling
         generation_kwargs = {