Add generate() functionality to TF 2.0 (#3063)
* add first copy past test to tf 2 generate * add tf top_k_top_p_filter fn * add generate function for TF * add generate function for TF * implemented generate for all models expect transfoXL * implemented generate for all models expect transfoXL * implemented generate for all models expect transfoXL * make style * change permission of test file to correct ones * delete ipdb * delete ipdb * fix bug and finish simple gpt2 integration test * clean test file * clean test file * make style * make style * make style * make style * change import style * change import style * make style * make style * add decorators * add decorators * fix tf ctrl bug dim => axis in TF * make style * make style * refactored test file * refactored test file * take out test_torch_tf_conversion if nothing is defined * take out test_torch_tf_conversion if nothing is defined * remove useless files * remove useless files * fix conflicts * fix conflicts * fix conflicts * fix conflicts * fix conflicts * solve conflicts * solve conflicts * fix conflicts * fix conflicts * merge conflicts * delete ipdb * exposed top_k_top_p_filtering fns * delete weirdly created w! file * add comment to test tf common modeling * fix conflicts * fix conflicts * make style * merge conflicts * make style * change tf.tensor.shape to shape_list(tensor)
This commit is contained in:
Patrick von Platen
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GitHub
GitHub
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b31f715019
commit
4134100363
@@ -37,7 +37,7 @@ if is_tf_available():
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class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel) if is_tf_available() else ()
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# all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel) if is_tf_available() else ()
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all_generative_model_classes = (TFGPT2LMHeadModel,) if is_tf_available() else ()
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class TFGPT2ModelTester(object):
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def __init__(
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@@ -89,6 +89,8 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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@@ -123,9 +125,11 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
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# hidden_dropout_prob=self.hidden_dropout_prob,
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# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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n_positions=self.max_position_embeddings,
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n_ctx=self.max_position_embeddings
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n_ctx=self.max_position_embeddings,
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# type_vocab_size=self.type_vocab_size,
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# initializer_range=self.initializer_range
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bos_token_id=self.bos_token_id,
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eos_token_ids=self.eos_token_id,
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)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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@@ -144,7 +148,11 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
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def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPT2Model(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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sequence_output = model(inputs)[0]
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inputs = [input_ids, None, input_mask] # None is the input for 'past'
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@@ -156,18 +164,22 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
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"sequence_output": sequence_output.numpy(),
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size]
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list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size],
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)
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def create_and_check_gpt2_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPT2LMHeadModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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prediction_scores = model(inputs)[0]
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result = {
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"prediction_scores": prediction_scores.numpy(),
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size]
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list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size],
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)
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def create_and_check_gpt2_double_head(
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@@ -188,7 +200,7 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
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lm_logits, mc_logits = model(inputs)[:2]
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result = {"lm_logits": lm_logits.numpy(), "mc_logits": mc_logits.numpy()}
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self.parent.assertListEqual(
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list(result["lm_logits"].shape), [self.batch_size, self.num_choices, self.seq_length, self.vocab_size]
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list(result["lm_logits"].shape), [self.batch_size, self.num_choices, self.seq_length, self.vocab_size],
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)
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self.parent.assertListEqual(list(result["mc_logits"].shape), [self.batch_size, self.num_choices])
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@@ -207,7 +219,11 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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inputs_dict = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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}
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return config, inputs_dict
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def setUp(self):
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@@ -234,3 +250,48 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
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for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = TFGPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
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self.assertIsNotNone(model)
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def prepare_generation_special_tokens():
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return {"bos_token_id": 50256, "eos_token_id": 50256}
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class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
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special_tokens = prepare_generation_special_tokens()
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@slow
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def test_lm_generate_distilgpt2(self):
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model = TFGPT2LMHeadModel.from_pretrained("distilgpt2")
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input_ids = tf.convert_to_tensor([[464, 1893]], dtype=tf.int32) # The president
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expected_output_ids = [
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464,
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1893,
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286,
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262,
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1578,
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1829,
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11,
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290,
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262,
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1893,
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286,
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262,
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1578,
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7526,
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11,
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423,
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587,
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287,
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262,
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2635,
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] # The president of the United States, and the president of the United Kingdom, have been in the White
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output_ids = model.generate(
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input_ids,
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do_sample=False,
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bos_token_id=self.special_tokens["bos_token_id"],
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eos_token_ids=self.special_tokens["eos_token_id"],
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)
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self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
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