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Generate: decoder-only models can generate with inputs_embeds (#21405)

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This commit is contained in:
Joao Gante
2023-02-01 21:50:38 +00:00
committed by GitHub
parent e5db7051a8
commit 92ce53aab8
3 changed files with 68 additions and 52 deletions
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40
tests/generation/test_utils.py
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@@ -2359,17 +2359,6 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
self.assertTrue(diff < 1e-4)
def test_decoder_generate_with_inputs_embeds(self):
article = """I need input_ids to generate"""
tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=5).to(torch_device)
input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
inputs_embeds = model.get_input_embeddings()(input_ids)
# cannot generate from `inputs_embeds` for decoder only
with self.assertRaises(ValueError):
model.generate(inputs_embeds=inputs_embeds)
def test_generate_input_ids_as_kwarg(self):
article = """I need input_ids to generate"""
tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
@@ -2417,8 +2406,10 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
def test_generate_too_many_encoder_kwargs(self):
article = """I need input_ids to generate"""
tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=10).to(torch_device)
tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart", max_length=10).to(
torch_device
)
input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
with self.assertRaises(ValueError):
model.generate(input_ids=input_ids, inputs_embeds=input_ids)
@@ -3128,3 +3119,26 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
eos_token_id = [873]
generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
self.assertTrue(expectation == len(generated_tokens[0]))
def test_generate_from_input_embeds_decoder_only(self):
# Note: the model must support generation from input embeddings
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = "Hello world"
input_ids = tokenizer.encode(text, return_tensors="pt")
# Traditional way of generating text
outputs_from_ids = model.generate(input_ids)
# Same thing, but from input embeddings
inputs_embeds = model.transformer.wte(input_ids)
outputs_from_embeds = model.generate(input_ids, inputs_embeds=inputs_embeds)
self.assertListEqual(outputs_from_ids.tolist(), outputs_from_embeds.tolist())
# But if we pass different inputs_embeds, we should get different outputs
torch.manual_seed(0)
random_embeds = torch.rand_like(inputs_embeds)
outputs_from_rand_embeds = model.generate(input_ids, inputs_embeds=random_embeds)
with self.assertRaises(AssertionError):
self.assertListEqual(outputs_from_rand_embeds.tolist(), outputs_from_embeds.tolist())