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Generation: fix test (#34369)

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* fix test

* fix copies
This commit is contained in:
Raushan Turganbay
2024-10-29 07:57:10 +01:00
committed by GitHub
parent fe76b60370
commit 808d6c50f8
4 changed files with 28 additions and 44 deletions
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44
tests/generation/test_utils.py
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@@ -671,29 +671,6 @@ class GenerationTesterMixin:
else:
self.assertTrue(output_generate.shape[-1] == self.max_new_tokens + inputs_dict["input_ids"].shape[-1])
# for VLMs inputs embeds won't match input ids unless images are encoded and merged with ids properly
# no quick fix available, since obtaining image embeddings step is very model-specific
if any(name in model.__class__.__name__.lower() for name in ("blip", "llava", "paligemma")):
prepare_inputs_for_generation_args = set(
inspect.signature(model.prepare_inputs_for_generation).parameters
)
# `inputs_embeds` input is well supported when `cache_positions` is used, because it means the modeling
# code is up to date with our most recent standards
if (
"inputs_embeds" in prepare_inputs_for_generation_args
and "cache_positions" in prepare_inputs_for_generation_args
):
input_embeds = model.get_input_embeddings()(inputs_dict["input_ids"])
beam_kwargs.update({"inputs_embeds": input_embeds})
output_generate2 = self._beam_sample_generate(
model=model,
input_ids=None,
inputs_dict={},
beam_kwargs=beam_kwargs,
)
torch.testing.assert_close(output_generate[:, input_embeds.shape[1] :], output_generate2)
@pytest.mark.generate
def test_beam_sample_generate_dict_output(self):
for model_class in self.all_generative_model_classes:
@@ -1570,7 +1547,8 @@ class GenerationTesterMixin:
)
@pytest.mark.generate
def test_generate_from_inputs_embeds_decoder_only(self):
@parameterized.expand([(1,), (2,)])
def test_generate_from_inputs_embeds_decoder_only(self, num_beams):
# When supported, tests that the decoder model can generate from `inputs_embeds` instead of `input_ids`
# if fails, you should probably update the `prepare_inputs_for_generation` function
for model_class in self.all_generative_model_classes:
@@ -1597,11 +1575,15 @@ class GenerationTesterMixin:
continue
input_ids = inputs_dict.pop("input_ids")
generation_kwargs = {
"return_dict_in_generate": True,
"output_scores": True,
"num_beams": num_beams,
"do_sample": False,
}
# Traditional way of generating text
outputs_from_ids = model.generate(
input_ids, max_new_tokens=5, return_dict_in_generate=True, output_scores=True
)
outputs_from_ids = model.generate(input_ids, max_new_tokens=5, **generation_kwargs)
self.assertEqual(outputs_from_ids.sequences.shape, (input_ids.shape[0], input_ids.shape[1] + 5))
# Same thing, but from input embeddings (`input_ids` is passed so the prompt is present in the output)
@@ -1610,8 +1592,7 @@ class GenerationTesterMixin:
input_ids,
inputs_embeds=inputs_embeds,
max_new_tokens=5,
return_dict_in_generate=True,
output_scores=True,
**generation_kwargs,
)
self.assertListEqual(outputs_from_ids.sequences.tolist(), outputs_from_embeds.sequences.tolist())
@@ -1622,15 +1603,14 @@ class GenerationTesterMixin:
input_ids,
inputs_embeds=random_embeds,
max_new_tokens=5,
return_dict_in_generate=True,
output_scores=True,
**generation_kwargs,
)
for i in range(len(outputs_from_rand_embeds.scores)):
self.assertFalse(torch.allclose(outputs_from_embeds.scores[i], outputs_from_rand_embeds.scores[i]))
# input_ids is not a required input -- if we don't pass it, the newly generated tokens will be the same
outputs_from_embeds_wo_ids = model.generate(
inputs_embeds=inputs_embeds, max_new_tokens=5, return_dict_in_generate=True, output_scores=True
inputs_embeds=inputs_embeds, max_new_tokens=5, **generation_kwargs
)
self.assertListEqual(
outputs_from_embeds.sequences[:, inputs_embeds.shape[1] :].tolist(),