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Generate: TF can now generate from embeddings in encoder-decoder models (#21475)

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This commit is contained in:
Joao Gante
2023-02-07 11:18:23 +00:00
committed by GitHub
parent 12eb528b5a
commit 1e4cf8bb44
4 changed files with 184 additions and 197 deletions
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178
tests/generation/test_utils.py
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@@ -40,7 +40,6 @@ if is_torch_available():
ImageGPTForCausalImageModeling,
Speech2TextForConditionalGeneration,
SpeechEncoderDecoderModel,
T5ForConditionalGeneration,
VisionEncoderDecoderModel,
top_k_top_p_filtering,
)
@@ -1792,6 +1791,7 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
# setting framework_dependent_parameters needs to be gated, just like its contents' imports
if is_torch_available():
framework_dependent_parameters = {
"AutoModelForCausalLM": AutoModelForCausalLM,
"AutoModelForSeq2SeqLM": AutoModelForSeq2SeqLM,
"LogitsProcessorList": LogitsProcessorList,
"MinLengthLogitsProcessor": MinLengthLogitsProcessor,
@@ -2094,182 +2094,6 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
output = generator(prompt, stop_sequence=" number")
self.assertEqual(output, [{"generated_text": "Hello I believe in in in number"}])
def test_max_new_tokens_encoder_decoder(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
self.assertEqual(list(input_ids.shape), [1, 29])
max_new_tokens = 3
bart_model.config.max_length = 20
bart_model.config.eos_token_id = None
# Encoder decoder call
outputs = bart_model.generate(input_ids, max_new_tokens=max_new_tokens)
# 1 BOS + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 4])
# Decoder only call
outputs = bart_model.generate(decoder_input_ids=input_ids, max_new_tokens=max_new_tokens)
# 29 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 32])
# Encoder decoder call > 20
outputs = bart_model.generate(max_new_tokens=max_new_tokens + 20)
# 1 BOS + 20 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
def test_max_new_tokens_decoder_only_contrastive_search_t5(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
t5_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
t5_model = T5ForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-t5").to(torch_device)
input_ids = t5_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
self.assertEqual(list(input_ids.shape), [1, 56])
max_new_tokens = 3
t5_model.config.max_length = 20
t5_model.config.eos_token_id = None
# Encoder decoder call
outputs = t5_model.generate(input_ids, max_new_tokens=max_new_tokens, penalty_alpha=0.6, top_k=4)
# 1 BOS + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 4])
# Decoder only call
outputs = t5_model.generate(
decoder_input_ids=input_ids, max_new_tokens=max_new_tokens, penalty_alpha=0.6, top_k=4
)
# 56 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 59])
# Encoder decoder call > 20
outputs = t5_model.generate(max_new_tokens=max_new_tokens + 20, penalty_alpha=0.6, top_k=4)
# 1 BOS + 20 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
def test_max_new_tokens_decoder_only_contrastive_search_bart(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
torch_device
)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
self.assertEqual(list(input_ids.shape), [1, 29])
max_new_tokens = 3
bart_model.config.max_length = 20
bart_model.config.eos_token_id = None
# Encoder decoder call
outputs = bart_model.generate(input_ids, max_new_tokens=max_new_tokens, penalty_alpha=0.6, top_k=4)
# 1 BOS + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 4])
# Decoder only call
outputs = bart_model.generate(
decoder_input_ids=input_ids, max_new_tokens=max_new_tokens, penalty_alpha=0.6, top_k=4
)
# 29 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 32])
# Encoder decoder call > 20
outputs = bart_model.generate(max_new_tokens=max_new_tokens + 20, penalty_alpha=0.6, top_k=4)
# 1 BOS + 20 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
def test_max_new_tokens_decoder_only_contrastive_search_gptj(self):
article = """Justin Timberlake."""
gptj_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gptj")
gptj_model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gptj").to(torch_device)
input_ids = gptj_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
self.assertEqual(list(input_ids.shape), [1, 9])
max_new_tokens = 3
gptj_model.config.max_length = 20
# call < 20
outputs = gptj_model.generate(input_ids, max_new_tokens=max_new_tokens, penalty_alpha=0.6, top_k=4)
# 9 input_ids + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 12])
# call > 20
outputs = gptj_model.generate(max_new_tokens=max_new_tokens + 20, penalty_alpha=0.6, top_k=4)
# 1 BOS token + 23 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
def test_max_new_tokens_decoder_only_contrastive_search_gpt2(self):
article = """Justin Timberlake."""
gpt2_tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
gpt2_model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
input_ids = gpt2_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
self.assertEqual(list(input_ids.shape), [1, 9])
max_new_tokens = 3
gpt2_model.config.max_length = 20
# call < 20
outputs = gpt2_model.generate(input_ids, max_new_tokens=max_new_tokens, penalty_alpha=0.6, top_k=4)
# 9 input_ids + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 12])
# call > 20
outputs = gpt2_model.generate(max_new_tokens=max_new_tokens + 20, penalty_alpha=0.6, top_k=4)
# 1 BOS token + 23 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
def test_max_new_tokens_decoder_only(self):
article = """Justin Timberlake."""
gpt2_tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
gpt2_model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
input_ids = gpt2_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
self.assertEqual(list(input_ids.shape), [1, 9])
max_new_tokens = 3
gpt2_model.config.max_length = 20
# call < 20
outputs = gpt2_model.generate(input_ids, max_new_tokens=max_new_tokens)
# 9 input_ids + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 12])
# call > 20
outputs = gpt2_model.generate(max_new_tokens=max_new_tokens + 20)
# 1 BOS token + 23 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
def test_encoder_decoder_generate_with_inputs_embeds(self):
article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart", max_length=5).to(
torch_device
)
model.config.eos_token_id = None
input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
inputs_embeds = model.get_input_embeddings()(input_ids)
output_sequences = model.generate(inputs_embeds=inputs_embeds)
# make sure model generated correctly until `max_length`
self.assertEqual(output_sequences.shape, (1, 5))
def test_encoder_decoder_generate_attention_mask(self):
articles = ["Timberlake", "Jessica Biel, welcome to parenthood among other things"]
tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")