Move test model folders (#17034)

* move test model folders (TODO: fix imports and others) * fix (potentially partially) imports (in model test modules) * fix (potentially partially) imports (in tokenization test modules) * fix (potentially partially) imports (in feature extraction test modules) * fix import utils.test_modeling_tf_core * fix path ../fixtures/ * fix imports about generation.test_generation_flax_utils * fix more imports * fix fixture path * fix get_test_dir * update module_to_test_file * fix get_tests_dir from wrong transformers.utils * update config.yml (CircleCI) * fix style * remove missing imports * update new model script * update check_repo * update SPECIAL_MODULE_TO_TEST_MAP * fix style * add __init__ * update self-scheduled * fix add_new_model scripts * check one way to get location back * python setup.py build install * fix import in test auto * update self-scheduled.yml * update slack notification script * Add comments about artifact names * fix for yolos Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
2022-05-03 14:42:02 +02:00
parent cd9274d010
commit 19420fd99e
408 changed files with 616 additions and 607 deletions
--- a/tests/models/gpt2/test_modeling_tf_gpt2.py
+++ b/tests/models/gpt2/test_modeling_tf_gpt2.py
@@ -0,0 +1,593 @@
+# coding=utf-8
+# 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 GPT2Config, is_tf_available
+from transformers.testing_utils import require_tf, slow
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import GPT2Tokenizer
+    from transformers.models.gpt2.modeling_tf_gpt2 import (
+        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
+        TFGPT2DoubleHeadsModel,
+        TFGPT2ForSequenceClassification,
+        TFGPT2LMHeadModel,
+        TFGPT2Model,
+    )
+    from transformers.tf_utils import shape_list
+
+
+class TFGPT2ModelTester:
+    def __init__(
+        self,
+        parent,
+    ):
+        self.parent = parent
+        self.batch_size = 13
+        self.seq_length = 7
+        self.is_training = True
+        self.use_token_type_ids = True
+        self.use_input_mask = True
+        self.use_labels = True
+        self.use_mc_token_ids = True
+        self.vocab_size = 99
+        self.hidden_size = 32
+        self.num_hidden_layers = 5
+        self.num_attention_heads = 4
+        self.intermediate_size = 37
+        self.hidden_act = "gelu"
+        self.hidden_dropout_prob = 0.1
+        self.attention_probs_dropout_prob = 0.1
+        self.max_position_embeddings = 512
+        self.type_vocab_size = 16
+        self.type_sequence_label_size = 2
+        self.initializer_range = 0.02
+        self.num_labels = 3
+        self.num_choices = 4
+        self.scope = None
+        self.bos_token_id = self.vocab_size - 1
+        self.eos_token_id = self.vocab_size - 1
+        self.pad_token_id = self.vocab_size - 1
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        mc_token_ids = None
+        if self.use_mc_token_ids:
+            mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = GPT2Config(
+            vocab_size=self.vocab_size,
+            n_embd=self.hidden_size,
+            n_layer=self.num_hidden_layers,
+            n_head=self.num_attention_heads,
+            # intermediate_size=self.intermediate_size,
+            # hidden_act=self.hidden_act,
+            # hidden_dropout_prob=self.hidden_dropout_prob,
+            # attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            n_positions=self.max_position_embeddings,
+            # type_vocab_size=self.type_vocab_size,
+            # initializer_range=self.initializer_range
+            bos_token_id=self.bos_token_id,
+            eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
+            return_dict=True,
+        )
+
+        head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
+
+        return (
+            config,
+            input_ids,
+            input_mask,
+            head_mask,
+            token_type_ids,
+            mc_token_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        )
+
+    def prepare_config_and_inputs_for_decoder(self):
+        (
+            config,
+            input_ids,
+            input_mask,
+            head_mask,
+            token_type_ids,
+            mc_token_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = self.prepare_config_and_inputs()
+
+        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
+        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+        return (
+            config,
+            input_ids,
+            input_mask,
+            head_mask,
+            token_type_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
+    def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+        model = TFGPT2Model(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+        result = model(inputs)
+
+        inputs = [input_ids, None, input_mask]  # None is the input for 'past'
+        result = model(inputs)
+
+        result = model(input_ids)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+        model = TFGPT2Model(config=config)
+
+        # first forward pass
+        outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
+        outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
+        outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
+
+        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
+        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
+
+        output, past = outputs.to_tuple()
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
+        next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
+
+        # append to next input_ids and token_type_ids
+        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
+        next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
+
+        output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
+        output_from_past = model(next_tokens, token_type_ids=next_token_types, past=past)["last_hidden_state"]
+
+        # select random slice
+        random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
+        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
+        output_from_past_slice = output_from_past[:, 0, random_slice_idx]
+
+        # test that outputs are equal for slice
+        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
+
+    def create_and_check_gpt2_model_attention_mask_past(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, *args
+    ):
+        model = TFGPT2Model(config=config)
+
+        # create attention mask
+        half_seq_length = self.seq_length // 2
+        attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
+        attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
+        attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
+
+        # first forward pass
+        output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
+
+        # change a random masked slice from input_ids
+        random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
+        random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
+        vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
+        condition = tf.transpose(
+            tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
+        )
+        input_ids = tf.where(condition, random_other_next_tokens, input_ids)
+
+        # append to next input_ids and attn_mask
+        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
+        attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=1)
+
+        # get two different outputs
+        output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
+        output_from_past = model(next_tokens, past=past, attention_mask=attn_mask)["last_hidden_state"]
+
+        # select random slice
+        random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
+        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
+        output_from_past_slice = output_from_past[:, 0, random_slice_idx]
+
+        # test that outputs are equal for slice
+        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-12)
+
+    def create_and_check_gpt2_model_past_large_inputs(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, *args
+    ):
+        model = TFGPT2Model(config=config)
+
+        input_ids = input_ids[:1, :]
+        input_mask = input_mask[:1, :]
+        token_type_ids = token_type_ids[:1, :]
+        self.batch_size = 1
+
+        # first forward pass
+        outputs = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, use_cache=True)
+
+        output, past = outputs.to_tuple()
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
+        next_attn_mask = ids_tensor((self.batch_size, 3), 2)
+        next_token_types = ids_tensor((self.batch_size, 3), self.type_vocab_size)
+
+        # append to next input_ids and token_type_ids
+        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
+        next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
+        next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
+
+        output_from_no_past = model(
+            next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
+        )["last_hidden_state"]
+        output_from_past = model(
+            next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past=past
+        )["last_hidden_state"]
+        self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
+
+        # select random slice
+        random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
+        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
+        output_from_past_slice = output_from_past[:, :, random_slice_idx]
+
+        # test that outputs are equal for slice
+        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
+
+    def create_and_check_gpt2_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+        model = TFGPT2LMHeadModel(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+        result = model(inputs)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_gpt2_xla_generate_fast(self, config, input_ids, *args):
+        config.eos_token_id = None
+        config.max_length = 10
+        model = TFGPT2LMHeadModel(config=config)
+
+        # make sure there are no pad tokens in prompt
+        input_ids = tf.where(input_ids != config.pad_token_id, input_ids, config.pad_token_id - 1)
+
+        generated = model.generate(input_ids)
+
+        generate_xla = tf.function(model.generate, jit_compile=True)
+        generated_xla = generate_xla(input_ids)
+
+        self.parent.assertListEqual(generated.numpy().tolist(), generated_xla.numpy().tolist())
+
+    def create_and_check_gpt2_double_head(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
+    ):
+        model = TFGPT2DoubleHeadsModel(config=config)
+
+        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
+        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
+        multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
+
+        inputs = {
+            "input_ids": multiple_choice_inputs_ids,
+            "mc_token_ids": mc_token_ids,
+            "attention_mask": multiple_choice_input_mask,
+            "token_type_ids": multiple_choice_token_type_ids,
+        }
+        result = model(inputs)
+        self.parent.assertEqual(
+            result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)
+        )
+        self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
+
+    def create_and_check_gpt2_for_sequence_classification(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
+    ):
+        config.num_labels = self.num_labels
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+            "labels": sequence_labels,
+        }
+        model = TFGPT2ForSequenceClassification(config)
+
+        result = model(inputs)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+
+        (
+            config,
+            input_ids,
+            input_mask,
+            head_mask,
+            token_type_ids,
+            mc_token_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+
+        inputs_dict = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "attention_mask": input_mask,
+        }
+        return config, inputs_dict
+
+
+@require_tf
+class TFGPT2ModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2ForSequenceClassification, TFGPT2DoubleHeadsModel)
+        if is_tf_available()
+        else ()
+    )
+    all_generative_model_classes = (TFGPT2LMHeadModel,) if is_tf_available() else ()
+    test_head_masking = False
+    test_onnx = True
+    onnx_min_opset = 10
+
+    def setUp(self):
+        self.model_tester = TFGPT2ModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_gpt2_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_model(*config_and_inputs)
+
+    def test_gpt2_model_past(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs)
+
+    def test_gpt2_model_att_mask_past(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs)
+
+    def test_gpt2_model_past_large_inputs(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_model_past_large_inputs(*config_and_inputs)
+
+    def test_gpt2_lm_head(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_lm_head(*config_and_inputs)
+
+    def test_gpt2_xla_generate_fast(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_xla_generate_fast(*config_and_inputs)
+
+    def test_gpt2_double_head(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_double_head(*config_and_inputs)
+
+    def test_model_common_attributes(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
+
+            if model_class in self.all_generative_model_classes:
+                x = model.get_output_embeddings()
+                assert isinstance(x, tf.keras.layers.Layer)
+                name = model.get_bias()
+                assert name is None
+            else:
+                x = model.get_output_embeddings()
+                assert x is None
+                name = model.get_bias()
+                assert name is None
+
+    def test_gpt2_sequence_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFGPT2Model.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+@require_tf
+class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
+    @slow
+    def test_lm_generate_greedy_distilgpt2_batch_special(self):
+        model = TFGPT2LMHeadModel.from_pretrained("distilgpt2")
+        tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
+
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+
+        sentences = ["Today is a beautiful day and", "Yesterday was"]
+        input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
+
+        generation_kwargs = {
+            "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids],
+            "no_repeat_ngram_size": 2,
+            "do_sample": False,
+            "repetition_penalty": 1.3,
+        }
+
+        output_ids = model.generate(input_ids, **generation_kwargs)
+
+        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        expected_output_string = [
+            "Today is a beautiful day and I am so happy to be able take part in this amazing event.",
+            "Yesterday was a very busy day for the first time since I started writing this post",
+        ]
+        self.assertListEqual(output_strings, expected_output_string)
+
+    @slow
+    def test_lm_generate_sample_distilgpt2_batch_special(self):
+        model = TFGPT2LMHeadModel.from_pretrained("distilgpt2")
+        tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
+
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+
+        sentences = ["Today is a beautiful day and", "Yesterday was"]
+        input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
+
+        generation_kwargs = {
+            "do_sample": True,
+            "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids],
+            "no_repeat_ngram_size": 2,
+            "repetition_penalty": 1.3,
+            "temperature": 1.5,
+            "top_k": 500,
+            "top_p": 0.9,
+            "seed": [42, 0],  # seed set -> deterministic sampling sequence -> deterministic generation
+        }
+
+        # forces the generation to happen on CPU, to avoid GPU-related quirks
+        with tf.device(":/CPU:0"):
+            output_ids = model.generate(input_ids, **generation_kwargs)
+
+        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+
+        expected_output_string = [
+            "Today is a beautiful day and we will make you feel very hot/terrific in all",
+            "Yesterday was another solid success as news coverage became standard American domestic television hit.",
+        ]
+        self.assertListEqual(output_strings, expected_output_string)
+
+    @slow
+    def test_lm_generate_greedy_distilgpt2_beam_search_special(self):
+        model = TFGPT2LMHeadModel.from_pretrained("distilgpt2")
+        tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
+
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+
+        sentences = ["Today is a beautiful day and", "Yesterday was"]
+        input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
+
+        generation_kwargs = {
+            "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids],
+            "no_repeat_ngram_size": 2,
+            "do_sample": False,
+            "num_beams": 2,
+        }
+
+        output_ids = model.generate(input_ids, **generation_kwargs)
+
+        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        expected_output_string = [
+            "Today is a beautiful day and a great day for all of us.\n\nI’m",
+            "Yesterday was the first day of the year for the second time in a row,",
+        ]
+        self.assertListEqual(output_strings, expected_output_string)
+
+    @slow
+    def test_lm_generate_gpt2_greedy_xla(self):
+        # TODO (Joao): convert this to an example with a batch size>1 with different input lengths that works (and fix
+        # the underlying problem)
+        model = TFGPT2LMHeadModel.from_pretrained("gpt2")
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+
+        sentences = ["The dog"]
+        expected_output_strings = [
+            "The dog was found in a field near the intersection of West and West Streets.\n\nThe dog",
+        ]
+        input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
+
+        output_ids = model.generate(input_ids, do_sample=False)
+        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        self.assertListEqual(output_strings, expected_output_strings)
+
+        xla_generate = tf.function(model.generate, jit_compile=True)
+        output_ids = xla_generate(input_ids, do_sample=False)
+        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        self.assertListEqual(output_strings, expected_output_strings)
+
+    @slow
+    def test_lm_generate_gpt2_sample_xla(self):
+        # NOTE: due to the small numerical differences that are natural when we compile to XLA, sampling the same
+        # output out of the same seed is far from guaranteed. We can, however, confirm that the results are sensible
+        # and that we can seed both versions.
+
+        # forces the generation to happen on CPU, to avoid GPU-related quirks
+        with tf.device(":/CPU:0"):
+            model = TFGPT2LMHeadModel.from_pretrained("gpt2")
+            tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+            tokenizer.pad_token = tokenizer.eos_token
+            tokenizer.padding_side = "left"
+
+            sentence = ["The dog"]
+            expected_output_string = [
+                "The dog owner asked why did our vet decide there needed to be extra ventilation inside because most puppies"
+            ]
+            expected_output_string_xla = [
+                "The dog has been named in connection with the murder of a 20-year-old man in!"
+            ]
+            input_ids = tokenizer(sentence, return_tensors="tf", padding=True).input_ids
+
+            output_ids = model.generate(input_ids, do_sample=True, seed=[7, 0])
+            output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+            self.assertListEqual(output_strings, expected_output_string)
+
+            xla_generate = tf.function(model.generate, jit_compile=True)
+            output_ids = xla_generate(input_ids, do_sample=True, seed=[7, 0])
+            output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+            self.assertListEqual(output_strings, expected_output_string_xla)