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[NLLB-MoE] model_type update for auto mapping (#22470)

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edit default model type and testing path set to hf-internal-testing
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Arthur 2023-03-30 15:36:07 +02:00 committed by GitHub
parent 11426641dc
commit 349e1242d9
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2 changed files with 4 additions and 4 deletions
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2
src/transformers/models/nllb_moe/configuration_nllb_moe.py
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@ -125,7 +125,7 @@ class NllbMoeConfig(PretrainedConfig):
>>> # Accessing the model configuration >>> # Accessing the model configuration
>>> configuration = model.config >>> configuration = model.config
```""" ```"""
model_type = "nllb_moe" model_type = "nllb-moe"
keys_to_ignore_at_inference = ["past_key_values"] keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

6
tests/models/nllb_moe/test_modeling_nllb_moe.py
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@ -354,14 +354,14 @@ class NllbMoeModelIntegrationTests(unittest.TestCase):
@cached_property @cached_property
def tokenizer(self): def tokenizer(self):
return NllbTokenizer.from_pretrained("ArthurZ/random-nllb-moe-2-experts") return NllbTokenizer.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")
@cached_property @cached_property
def big_model(self): def big_model(self):
return NllbMoeForConditionalGeneration.from_pretrained("facebook/nllb-moe-54b") return NllbMoeForConditionalGeneration.from_pretrained("facebook/nllb-moe-54b")
def inference_no_head(self): def inference_no_head(self):
model = NllbMoeModel.from_pretrained("ArthurZ/random-nllb-moe-2-experts").eval() model = NllbMoeModel.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts").eval()
with torch.no_grad(): with torch.no_grad():
output = model(**self.model_inputs) output = model(**self.model_inputs)
# fmt: off # fmt: off
@ -382,7 +382,7 @@ class NllbMoeModelIntegrationTests(unittest.TestCase):
and `transformers` implementation of NLLB-MoE transformers. We only check the logits and `transformers` implementation of NLLB-MoE transformers. We only check the logits
of the second sample of the batch, as it is padded. of the second sample of the batch, as it is padded.
""" """
model = NllbMoeForConditionalGeneration.from_pretrained("ArthurZ/random-nllb-moe-2-experts").eval() model = NllbMoeForConditionalGeneration.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts").eval()
with torch.no_grad(): with torch.no_grad():
output = model(**self.model_inputs) output = model(**self.model_inputs)