init

transformers/tests/pipelines/test_pipelines_text2text_generation.py

@@ -0,0 +1,139 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers import (
+    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    Text2TextGenerationPipeline,
+    pipeline,
+)
+from transformers.testing_utils import is_pipeline_test, require_torch
+from transformers.utils import is_torch_available
+
+from .test_pipelines_common import ANY
+
+
+if is_torch_available():
+    import torch
+
+
+@is_pipeline_test
+class Text2TextGenerationPipelineTests(unittest.TestCase):
+    model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
+
+    def get_test_pipeline(
+        self,
+        model,
+        tokenizer=None,
+        image_processor=None,
+        feature_extractor=None,
+        processor=None,
+        dtype="float32",
+    ):
+        generator = Text2TextGenerationPipeline(
+            model=model,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            image_processor=image_processor,
+            processor=processor,
+            dtype=dtype,
+            max_new_tokens=20,
+        )
+        return generator, ["Something to write", "Something else"]
+
+    def run_pipeline_test(self, generator, _):
+        outputs = generator("Something there")
+        self.assertEqual(outputs, [{"generated_text": ANY(str)}])
+        # These are encoder decoder, they don't just append to incoming string
+        self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))
+
+        outputs = generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
+        self.assertEqual(
+            outputs,
+            [
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+            ],
+        )
+
+        outputs = generator(
+            ["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
+        )
+        self.assertEqual(
+            outputs,
+            [
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+            ],
+        )
+
+        with self.assertRaises(TypeError):
+            generator(4)
+
+    @require_torch
+    def test_small_model_pt(self):
+        generator = pipeline(
+            "text2text-generation",
+            model="patrickvonplaten/t5-tiny-random",
+            num_beams=1,
+            max_new_tokens=9,
+        )
+        # do_sample=False necessary for reproducibility
+        outputs = generator("Something there", do_sample=False)
+        self.assertEqual(outputs, [{"generated_text": ""}])
+
+        num_return_sequences = 3
+        outputs = generator(
+            "Something there",
+            num_return_sequences=num_return_sequences,
+            num_beams=num_return_sequences,
+        )
+        target_outputs = [
+            {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
+            {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
+            {"generated_text": ""},
+        ]
+        self.assertEqual(outputs, target_outputs)
+
+        outputs = generator("This is a test", do_sample=True, num_return_sequences=2, return_tensors=True)
+        self.assertEqual(
+            outputs,
+            [
+                {"generated_token_ids": ANY(torch.Tensor)},
+                {"generated_token_ids": ANY(torch.Tensor)},
+            ],
+        )
+        generator.tokenizer.pad_token_id = generator.model.config.eos_token_id
+        generator.tokenizer.pad_token = "<pad>"
+        outputs = generator(
+            ["This is a test", "This is a second test"],
+            do_sample=True,
+            num_return_sequences=2,
+            batch_size=2,
+            return_tensors=True,
+        )
+        self.assertEqual(
+            outputs,
+            [
+                [
+                    {"generated_token_ids": ANY(torch.Tensor)},
+                    {"generated_token_ids": ANY(torch.Tensor)},
+                ],
+                [
+                    {"generated_token_ids": ANY(torch.Tensor)},
+                    {"generated_token_ids": ANY(torch.Tensor)},
+                ],
+            ],
+        )