SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

use torch.testing.assertclose instead to get more details about error in cis (#35659)

Browse Source 
* use torch.testing.assertclose instead to get more details about error in cis

* fix

* style

* test_all

* revert for I bert

* fixes and updates

* more image processing fixes

* more image processors

* fix mamba and co

* style

* less strick

* ok I won't be strict

* skip and be done

* up
This commit is contained in:
Arthur
2025-01-24 16:55:28 +01:00
committed by GitHub
parent 72d1a4cd53
commit b912f5ee43
255 changed files with 1048 additions and 969 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
tests/models/blip_2/test_modeling_blip_2.py
View File

@@ -901,7 +901,7 @@ class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationT
next_logits_with_padding = model(**model_kwargs, pixel_values=pixel_values).logits[:, -1, :]
# They should result in very similar logits
self.assertTrue(torch.allclose(next_logits_wo_padding, next_logits_with_padding, atol=1e-5))
torch.testing.assert_close(next_logits_wo_padding, next_logits_with_padding, rtol=1e-5, atol=1e-5)
@unittest.skip("BLIP2 cannot generate only from input ids, and requires pixel values in all cases to be present")
@parameterized.expand([("greedy", 1), ("beam search", 2)])
@@ -2215,8 +2215,8 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
# verify
expected_scores = torch.Tensor([[0.0238, 0.9762]])
self.assertTrue(torch.allclose(torch.nn.Softmax()(out_itm[0].cpu()), expected_scores, rtol=1e-3, atol=1e-3))
self.assertTrue(torch.allclose(out[0].cpu(), torch.Tensor([[0.4406]]), rtol=1e-3, atol=1e-3))
torch.testing.assert_close(torch.nn.Softmax()(out_itm[0].cpu()), expected_scores, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(out[0].cpu(), torch.Tensor([[0.4406]]), rtol=1e-3, atol=1e-3)
@require_torch_accelerator
@require_torch_fp16
@@ -2235,10 +2235,8 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
# verify
expected_scores = torch.Tensor([[0.0239, 0.9761]])
self.assertTrue(
torch.allclose(torch.nn.Softmax()(out_itm[0].cpu().float()), expected_scores, rtol=1e-3, atol=1e-3)
)
self.assertTrue(torch.allclose(out[0].cpu().float(), torch.Tensor([[0.4406]]), rtol=1e-3, atol=1e-3))
torch.testing.assert_close(torch.nn.Softmax()(out_itm[0].cpu().float()), expected_scores, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(out[0].cpu().float(), torch.Tensor([[0.4406]]), rtol=1e-3, atol=1e-3)
@require_torch_accelerator
@require_torch_fp16