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Replace legacy tensor.Tensor with torch.tensor/torch.empty (#12027)

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* Replace legacy torch.Tensor constructor with torch.{tensor, empty}

* Remove torch.Tensor in examples
This commit is contained in:
Mario Šaško
2021-06-08 05:58:38 -07:00
committed by GitHub
parent e33085d648
commit f5eec0d8e9
13 changed files with 26 additions and 22 deletions
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16
tests/test_modeling_bart.py
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@@ -308,14 +308,16 @@ class BartHeadTests(unittest.TestCase):
max_position_embeddings=48,
)
lm_model = BartForConditionalGeneration(config).to(torch_device)
context = torch.Tensor([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]).long().to(torch_device)
summary = torch.Tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]).long().to(torch_device)
context = torch.tensor(
[[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], device=torch_device, dtype=torch.long
)
summary = torch.tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], device=torch_device, dtype=torch.long)
outputs = lm_model(input_ids=context, decoder_input_ids=summary, labels=summary)
expected_shape = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_generate_beam_search(self):
input_ids = torch.Tensor([[71, 82, 2], [68, 34, 2]]).long().to(torch_device)
input_ids = torch.tensor([[71, 82, 2], [68, 34, 2]], device=torch_device, dtype=torch.long)
config = BartConfig(
vocab_size=self.vocab_size,
d_model=24,
@@ -345,7 +347,7 @@ class BartHeadTests(unittest.TestCase):
self.assertEqual(generated_ids.shape, (input_ids.shape[0], max_length))
def test_shift_tokens_right(self):
input_ids = torch.Tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]).long()
input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long)
shifted = shift_tokens_right(input_ids, 1, 2)
n_pad_before = input_ids.eq(1).float().sum()
n_pad_after = shifted.eq(1).float().sum()
@@ -358,8 +360,8 @@ class BartHeadTests(unittest.TestCase):
tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
examples = [" Hello world", " DomDramg"] # need leading spaces for equality
fairseq_results = [
torch.Tensor([0, 20920, 232, 2]),
torch.Tensor([0, 11349, 495, 4040, 571, 2]),
torch.tensor([0, 20920, 232, 2]),
torch.tensor([0, 11349, 495, 4040, 571, 2]),
]
for ex, desired_result in zip(examples, fairseq_results):
bart_toks = tokenizer.encode(ex, return_tensors="pt").squeeze()
@@ -614,7 +616,7 @@ class BartModelIntegrationTests(unittest.TestCase):
batched_logits = outputs.logits
expected_shape = torch.Size((2, 3))
self.assertEqual(batched_logits.shape, expected_shape)
expected_slice = torch.Tensor([[0.1907, 1.4342, -1.0289]]).to(torch_device)
expected_slice = torch.tensor([[0.1907, 1.4342, -1.0289]], device=torch_device)
logits_arr = batched_logits[0].detach()
# Test that padding does not change results