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TF: XLA beam search + most generation-compatible models are now also XLA-generate-compatible (#17857)

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* working beam search 🎉

* XLA generation compatible with ALL classes

* add xla generation slow test
This commit is contained in:
Joao Gante
2022-06-29 12:41:01 +01:00
committed by GitHub
parent b8142753f9
commit e6d27ca5c8
16 changed files with 356 additions and 301 deletions
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34
tests/models/bart/test_modeling_tf_bart.py
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@@ -152,23 +152,6 @@ class TFBartModelTester:
# 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_bart_xla_generate_fast(self, config, input_ids, *args):
config.eos_token_id = None # Generate until max length
config.max_length = 10
config.do_sample = False
config.num_beams = 1
model = TFBartForConditionalGeneration(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 prepare_bart_inputs_dict(
config,
@@ -310,10 +293,6 @@ class TFBartModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestC
models_equal = False
self.assertTrue(models_equal)
def test_bart_model_xla_generate_fast(self):
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_bart_xla_generate_fast(config, inputs["input_ids"])
def test_saved_model_creation(self):
# This test is too long (>30sec) and makes fail the CI
pass
@@ -703,10 +682,8 @@ class FasterTFBartModelIntegrationTests(unittest.TestCase):
result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0]
assert result == EXPECTED
def test_xsum_1_1_xla_greedy_generation(self):
# TODO (Joao): this is temporary test, while XLA beam search is not operational. Move the XLA==non-XLA
# comparisons to the other tests after enabling XLA beam search.
# Note -- `no_repeat_ngram_size` has to be disabled, since it is not compatible with XLA
def test_xsum_1_1_xla_generation(self):
# same test as above, but with `no_repeat_ngram_size=0` (not compatible with XLA) and XLA comparison enabled
model = self.xsum_1_1_model
assert model.model.decoder.embed_tokens._layer == model.model.shared
ARTICLE = (
@@ -748,15 +725,16 @@ class FasterTFBartModelIntegrationTests(unittest.TestCase):
)
EXPECTED = (
" The International Criminal Court (ICC) has announced that it is to be investigated by the International"
" Criminal Court (ICC) over claims that the Palestinian genocide."
" Criminal Court (ICC) over allegations of war crimes."
)
dct = self.tok(ARTICLE, return_tensors="tf")
generated_ids = model.generate(**dct, num_beams=1, no_repeat_ngram_size=0)
generated_ids = model.generate(**dct, num_beams=4, no_repeat_ngram_size=0)
result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0]
assert result == EXPECTED
xla_generate = tf.function(model.generate, jit_compile=True)
generated_ids = xla_generate(**dct, num_beams=1, no_repeat_ngram_size=0)
generated_ids = xla_generate(**dct, num_beams=4, no_repeat_ngram_size=0)
result = self.tok.batch_decode(generated_ids, skip_special_tokens=True)[0]
assert result == EXPECTED

43
tests/models/gpt2/test_modeling_tf_gpt2.py
View File

@@ -294,21 +294,6 @@ class TFGPT2ModelTester:
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 # Generate until max length
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
):
@@ -408,10 +393,6 @@ class TFGPT2ModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestC
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)
@@ -653,3 +634,27 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
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)
@slow
def test_lm_generate_gpt2_beam_search_xla(self):
model = TFGPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
sentences = ["The dog", "The flying machine"]
expected_output_strings = [
"The dog was found in the backyard of a home in the 6500 block of South Main Street",
"The flying machine is a very powerful machine, but it's not a very powerful machine. It's",
]
input_ids = tokenizer(sentences, return_tensors="tf", padding=True)
output_ids = model.generate(**input_ids, do_sample=False, num_beams=2)
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, num_beams=2)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
self.assertListEqual(output_strings, expected_output_strings)

58
tests/models/t5/test_modeling_tf_t5.py
View File

@@ -227,23 +227,6 @@ class TFT5ModelTester:
# 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_t5_xla_generate_fast(self, config, input_ids, *args):
config.eos_token_id = None # Generate until max length
config.max_length = 10
config.do_sample = False
config.num_beams = 1
model = TFT5ForConditionalGeneration(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 + 5)
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 prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, token_labels) = config_and_inputs
@@ -304,10 +287,6 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
self.model_tester.create_and_check_t5_decoder_model_past_large_inputs(*config_and_inputs)
def test_t5_model_xla_generate_fast(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_t5_xla_generate_fast(*config_and_inputs)
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -594,6 +573,43 @@ class TFT5GenerationIntegrationTests(unittest.TestCase):
self.assertListEqual(expected_output_string, output_strings)
@slow
def test_beam_search_xla_generate_simple(self):
model = TFT5ForConditionalGeneration.from_pretrained("t5-small")
tokenizer = T5Tokenizer.from_pretrained("t5-small")
# tests XLA with task specific arguments
task_specific_config = getattr(model.config, "task_specific_params", {})
translation_config = task_specific_config.get("translation_en_to_fr", {})
model.config.update(translation_config)
# two examples with different lengths to confirm that attention masks are operational in XLA
sentences = [
model.config.prefix + "Today is a beautiful day.",
model.config.prefix + "I have four cats, three dogs, two birds, and a horse.",
]
input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids
# xla_generate = tf.function(model.generate, jit_compile=True)
xla_generate = tf.function(model.generate)
# TODO (joao): there is something not quite right with XLA T5 -- as we increase `max_length` the two outputs
# drift appart, where the XLA version clearly degrades its quality. XLA-related variables look fine (they are
# being padded and filled in the right places). This also happens in other generation modes. Investigate.
output_ids = model.generate(input_ids, num_beams=2, max_length=9)
output_ids_xla = xla_generate(input_ids, num_beams=2, max_length=9)
output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
output_strings_xla = tokenizer.batch_decode(output_ids_xla, skip_special_tokens=True)
expected_output_string = [
"Aujourd'hui est une belle journée.",
"J'ai quatre chats,",
]
self.assertListEqual(expected_output_string, output_strings)
self.assertListEqual(expected_output_string, output_strings_xla)
@slow
def test_beam_search_generate(self):
model = TFT5ForConditionalGeneration.from_pretrained("t5-small")