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Add FlaxBartForCausalLM (#15995)

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* add causal lm

* add CausalLM tests

* Add FlaxBartForCausalLM

* Add EncoderDecoder model tests

* change docstring

* make repo-consistency

* suggested changes

* remove jax ops

* correction

* rename pre-trained decoder model
This commit is contained in:
Sanchit Gandhi
2022-03-09 19:53:01 +01:00
committed by GitHub
parent 50dd314d93
commit b256f3518d
10 changed files with 543 additions and 4 deletions
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119
tests/speech_encoder_decoder/test_modeling_flax_speech_encoder_decoder.py
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@@ -21,6 +21,7 @@ import numpy as np
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow, torch_device
from ..bart.test_modeling_flax_bart import FlaxBartStandaloneDecoderModelTester
from ..gpt2.test_modeling_flax_gpt2 import FlaxGPT2ModelTester
from ..test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..wav2vec2.test_modeling_flax_wav2vec2 import FlaxWav2Vec2ModelTester
@@ -28,6 +29,7 @@ from ..wav2vec2.test_modeling_flax_wav2vec2 import FlaxWav2Vec2ModelTester
if is_flax_available():
from transformers import (
FlaxBartForCausalLM,
FlaxGPT2LMHeadModel,
FlaxSpeechEncoderDecoderModel,
FlaxWav2Vec2Model,
@@ -553,3 +555,120 @@ class FlaxWav2Vec2GPT2ModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits_loaded.numpy(), 4e-2)
@require_flax
class FlaxWav2Vec2BartModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
"facebook/wav2vec2-large-lv60", "bart-large"
)
batch_size = 13
input_values = floats_tensor([batch_size, 512], model.config.encoder.vocab_size)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs = {
"inputs": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return model, inputs
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = FlaxWav2Vec2Model(config)
decoder_model = FlaxBartForCausalLM(decoder_config)
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
model_tester_encoder = FlaxWav2Vec2ModelTester(self, batch_size=13)
model_tester_decoder = FlaxBartStandaloneDecoderModelTester(self, batch_size=13)
encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder()
(config, inputs, attention_mask) = encoder_config_and_inputs
(
decoder_config,
decoder_input_ids,
decoder_attention_mask,
encoder_hidden_states,
encoder_attention_mask,
) = decoder_config_and_inputs
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
return {
"config": config,
"inputs": inputs,
"attention_mask": attention_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"encoder_hidden_states": encoder_hidden_states,
}
@slow
def test_flaxwav2vec2bart_pt_flax_equivalence(self):
pt_model = SpeechEncoderDecoderModel.from_pretrained("patrickvonplaten/wav2vec2-2-bart-large")
fx_model = FlaxSpeechEncoderDecoderModel.from_pretrained(
"patrickvonplaten/wav2vec2-2-bart-large", from_pt=True
)
pt_model.to(torch_device)
pt_model.eval()
# prepare inputs
batch_size = 13
input_values = floats_tensor([batch_size, 512], fx_model.config.encoder.vocab_size)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], fx_model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs_dict = {
"inputs": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
flax_inputs = inputs_dict
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
pt_logits = pt_outputs.logits
pt_outputs = pt_outputs.to_tuple()
fx_outputs = fx_model(**inputs_dict)
fx_logits = fx_outputs.logits
fx_outputs = fx_outputs.to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits.numpy(), 4e-2)
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**inputs_dict)
fx_logits_loaded = fx_outputs_loaded.logits
fx_outputs_loaded = fx_outputs_loaded.to_tuple()
self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits_loaded, pt_logits.numpy(), 4e-2)
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = SpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_flax=True)
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
pt_logits_loaded = pt_outputs_loaded.logits
pt_outputs_loaded = pt_outputs_loaded.to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits_loaded.numpy(), 4e-2)