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Forbid PretrainedConfig from saving generate parameters; Update deprecations in generate-related code 🧹 (#32659)

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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Joao Gante
2024-08-23 11:12:53 +01:00
committed by GitHub
parent 22e6f14525
commit 970a16ec7f
53 changed files with 195 additions and 670 deletions
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18
tests/generation/test_utils.py
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@@ -1576,6 +1576,11 @@ class GenerationTesterMixin:
# 3. ignore `token_type_ids` for simplicity
# 4. ignore `forced_eos_token_id`, which requires further manipulation of the continuation inputs and is
# active by default on some models
# 5. ignore `encoder_no_repeat_ngram_size`, which is set by default in some encoder-decoder models. When
# we use their decoder as a stand-alone model, `encoder_no_repeat_ngram_size` actually prevents
# repetition exclusively from the prompt. This test relies on comparing one call vs 2 calls
# with cache, what is considered a prompt is different in the two cases.
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
@@ -1583,6 +1588,7 @@ class GenerationTesterMixin:
model.eval()
model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
model.generation_config.forced_eos_token_id = None
model.generation_config.encoder_no_repeat_ngram_size = 0
model.generation_config.use_cache = True
# If "past_key_values" is not returned, skip the test (e.g. RWKV uses a different cache name and format)
@@ -2846,7 +2852,7 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
def test_default_max_length_warning(self):
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model.config.pad_token_id = tokenizer.eos_token_id
model.generation_config.pad_token_id = tokenizer.eos_token_id
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
@@ -2873,8 +2879,8 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
assistant = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model.config.pad_token_id = tokenizer.eos_token_id
assistant.config.pad_token_id = tokenizer.eos_token_id
model.generation_config.pad_token_id = tokenizer.eos_token_id
assistant.generation_config.pad_token_id = tokenizer.eos_token_id
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
@@ -2895,8 +2901,8 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
assistant = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model.config.pad_token_id = tokenizer.eos_token_id
assistant.config.pad_token_id = tokenizer.eos_token_id
model.generation_config.pad_token_id = tokenizer.eos_token_id
assistant.generation_config.pad_token_id = tokenizer.eos_token_id
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")
@@ -2922,7 +2928,7 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
# PT-only test: TF doesn't support assisted decoding yet.
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
model.config.pad_token_id = tokenizer.eos_token_id
model.generation_config.pad_token_id = tokenizer.eos_token_id
text = "Hello world"
tokenized_inputs = tokenizer([text], return_tensors="pt")