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
committed by
GitHub
GitHub
parent
b31f715019
commit
4134100363
@@ -18,6 +18,7 @@ import copy
|
||||
import os
|
||||
import random
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
from transformers import is_tf_available, is_torch_available
|
||||
|
||||
@@ -28,6 +29,8 @@ if is_tf_available():
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
|
||||
from transformers import tf_top_k_top_p_filtering
|
||||
|
||||
if _tf_gpu_memory_limit is not None:
|
||||
gpus = tf.config.list_physical_devices("GPU")
|
||||
for gpu in gpus:
|
||||
@@ -56,6 +59,7 @@ class TFModelTesterMixin:
|
||||
|
||||
model_tester = None
|
||||
all_model_classes = ()
|
||||
all_generative_model_classes = ()
|
||||
test_torchscript = True
|
||||
test_pruning = True
|
||||
test_resize_embeddings = True
|
||||
@@ -216,7 +220,7 @@ class TFModelTesterMixin:
|
||||
outputs_dict = model(inputs_dict)
|
||||
|
||||
inputs_keywords = copy.deepcopy(inputs_dict)
|
||||
input_ids = inputs_keywords.pop("input_ids" if not self.is_encoder_decoder else "decoder_input_ids", None)
|
||||
input_ids = inputs_keywords.pop("input_ids" if not self.is_encoder_decoder else "decoder_input_ids", None,)
|
||||
outputs_keywords = model(input_ids, **inputs_keywords)
|
||||
|
||||
output_dict = outputs_dict[0].numpy()
|
||||
@@ -299,7 +303,7 @@ class TFModelTesterMixin:
|
||||
self.assertEqual(model.config.output_hidden_states, True)
|
||||
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
|
||||
self.assertListEqual(
|
||||
list(hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size]
|
||||
list(hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size],
|
||||
)
|
||||
|
||||
def test_model_common_attributes(self):
|
||||
@@ -316,7 +320,10 @@ class TFModelTesterMixin:
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0]
|
||||
first, second = (
|
||||
model(inputs_dict, training=False)[0],
|
||||
model(inputs_dict, training=False)[0],
|
||||
)
|
||||
out_1 = first.numpy()
|
||||
out_2 = second.numpy()
|
||||
out_1 = out_1[~np.isnan(out_1)]
|
||||
@@ -338,9 +345,9 @@ class TFModelTesterMixin:
|
||||
x = wte([input_ids, None, None, None], mode="embedding")
|
||||
except Exception:
|
||||
if hasattr(self.model_tester, "embedding_size"):
|
||||
x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32)
|
||||
x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32,)
|
||||
else:
|
||||
x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
|
||||
x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32,)
|
||||
return x
|
||||
|
||||
def test_inputs_embeds(self):
|
||||
@@ -366,6 +373,37 @@ class TFModelTesterMixin:
|
||||
|
||||
model(inputs_dict)
|
||||
|
||||
def test_lm_head_model_random_generate(self):
|
||||
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
input_ids = inputs_dict.get(
|
||||
"input_ids", None
|
||||
) # TODO (PVP): ugly workaround to make code work for t5 for the moment - has to changed when t5 is fixed.
|
||||
|
||||
for model_class in self.all_generative_model_classes:
|
||||
# TODO (PVP): add beam search tests when beam search is implemented
|
||||
model = model_class(config)
|
||||
|
||||
if config.bos_token_id is None:
|
||||
with self.assertRaises(AssertionError):
|
||||
model.generate(max_length=5)
|
||||
# batch_size = 1
|
||||
self._check_generated_tokens(model.generate(input_ids))
|
||||
else:
|
||||
# batch_size = 1
|
||||
self._check_generated_tokens(model.generate(max_length=5))
|
||||
# batch_size = 1, num_beams > 1
|
||||
|
||||
# batch_size > 1, sample
|
||||
self._check_generated_tokens(model.generate(input_ids, num_return_sequences=3))
|
||||
# batch_size > 1, greedy
|
||||
self._check_generated_tokens(model.generate(input_ids, do_sample=False, num_return_sequences=3))
|
||||
|
||||
def _check_generated_tokens(self, output_ids):
|
||||
for token_id in output_ids[0].numpy().tolist():
|
||||
self.assertGreaterEqual(token_id, 0)
|
||||
self.assertLess(token_id, self.model_tester.vocab_size)
|
||||
|
||||
|
||||
def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
|
||||
"""Creates a random int32 tensor of the shape within the vocab size."""
|
||||
@@ -383,3 +421,98 @@ def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
|
||||
output = tf.constant(values, shape=shape, dtype=dtype if dtype is not None else tf.int32)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
@require_tf
|
||||
class UtilsFunctionsTest(unittest.TestCase):
|
||||
|
||||
# tests whether the top_k_top_p_filtering function behaves as expected
|
||||
def test_top_k_top_p_filtering(self):
|
||||
logits = tf.convert_to_tensor(
|
||||
[
|
||||
[
|
||||
8.2220991, # 3rd highest value; idx. 0
|
||||
-0.5620044,
|
||||
5.23229752,
|
||||
4.0386393,
|
||||
-6.8798378,
|
||||
-0.54785802,
|
||||
-3.2012153,
|
||||
2.92777176,
|
||||
1.88171953,
|
||||
7.35341276, # 5th highest value; idx. 9
|
||||
8.43207833, # 2nd highest value; idx. 10
|
||||
-9.85711836,
|
||||
-5.96209236,
|
||||
-1.13039161,
|
||||
-7.1115294,
|
||||
-0.8369633,
|
||||
-5.3186408,
|
||||
7.06427407,
|
||||
0.81369344,
|
||||
-0.82023817,
|
||||
-5.9179796,
|
||||
0.58813443,
|
||||
-6.99778438,
|
||||
4.71551189,
|
||||
-0.18771637,
|
||||
7.44020759, # 4th highest value; idx. 25
|
||||
9.38450987, # 1st highest value; idx. 26
|
||||
2.12662941,
|
||||
-9.32562038,
|
||||
2.35652522,
|
||||
], # cummulative prob of 5 highest values <= 0.6
|
||||
[
|
||||
0.58425518,
|
||||
4.53139238,
|
||||
-5.57510464,
|
||||
-6.28030699,
|
||||
-7.19529503,
|
||||
-4.02122551,
|
||||
1.39337037,
|
||||
-6.06707057,
|
||||
1.59480517,
|
||||
-9.643119,
|
||||
0.03907799,
|
||||
0.67231762,
|
||||
-8.88206726,
|
||||
6.27115922, # 4th highest value; idx. 13
|
||||
2.28520723,
|
||||
4.82767506,
|
||||
4.30421368,
|
||||
8.8275313, # 2nd highest value; idx. 17
|
||||
5.44029958, # 5th highest value; idx. 18
|
||||
-4.4735794,
|
||||
7.38579536, # 3rd highest value; idx. 20
|
||||
-2.91051663,
|
||||
2.61946077,
|
||||
-2.5674762,
|
||||
-9.48959302,
|
||||
-4.02922645,
|
||||
-1.35416918,
|
||||
9.67702323, # 1st highest value; idx. 27
|
||||
-5.89478553,
|
||||
1.85370467,
|
||||
], # cummulative prob of 5 highest values <= 0.6
|
||||
],
|
||||
dtype=tf.float32,
|
||||
)
|
||||
|
||||
non_inf_expected_idx = tf.convert_to_tensor(
|
||||
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]], dtype=tf.int32,
|
||||
) # expected non filtered idx as noted above
|
||||
|
||||
non_inf_expected_output = tf.convert_to_tensor(
|
||||
[8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
|
||||
dtype=tf.float32,
|
||||
) # expected non filtered values as noted above
|
||||
|
||||
output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
|
||||
|
||||
non_inf_output = output[output != -float("inf")]
|
||||
non_inf_idx = tf.cast(
|
||||
tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))), dtype=tf.int32,
|
||||
)
|
||||
|
||||
tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
|
||||
tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)
|
||||
|
||||
Reference in New Issue
Block a user