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Avoid flaky generation sampling tests (#21445)

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* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-02-03 22:01:25 +01:00
committed by GitHub
parent 31c351c4d3
commit 59d5edef34
2 changed files with 3 additions and 3 deletions
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2
tests/generation/test_utils.py
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@@ -780,7 +780,7 @@ class GenerationTesterMixin:
forced_eos_token_id=model.config.forced_eos_token_id, forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length, max_length=max_length,
) )
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1) logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2)
# check `generate()` and `sample()` are equal # check `generate()` and `sample()` are equal
output_sample, output_generate = self._sample_generate( output_sample, output_generate = self._sample_generate(

4
tests/models/switch_transformers/test_modeling_switch_transformers.py
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@@ -621,7 +621,7 @@ class SwitchTransformersModelTest(ModelTesterMixin, GenerationTesterMixin, unitt
config.forced_eos_token_id = None config.forced_eos_token_id = None
model = model_class.from_pretrained("google/switch-base-8").to(torch_device).eval() model = model_class.from_pretrained("google/switch-base-8").to(torch_device).eval()
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1) logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2)
num_return_sequences = 2 num_return_sequences = 2
if model.config.is_encoder_decoder: if model.config.is_encoder_decoder:
@@ -670,7 +670,7 @@ class SwitchTransformersModelTest(ModelTesterMixin, GenerationTesterMixin, unitt
config.eos_token_id = None config.eos_token_id = None
config.forced_eos_token_id = None config.forced_eos_token_id = None
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1) logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2)
model = model_class.from_pretrained("google/switch-base-8").to(torch_device).eval() model = model_class.from_pretrained("google/switch-base-8").to(torch_device).eval()