wrap forward passes with torch.no_grad() (#19439)
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@@ -568,6 +568,7 @@ class VisualBertModelIntegrationTest(unittest.TestCase):
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attention_mask = torch.tensor([1] * 6).reshape(1, -1)
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visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
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with torch.no_grad():
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output = model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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@@ -606,6 +607,7 @@ class VisualBertModelIntegrationTest(unittest.TestCase):
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attention_mask = torch.tensor([1] * 6).reshape(1, -1)
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visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
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with torch.no_grad():
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output = model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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@@ -637,6 +639,7 @@ class VisualBertModelIntegrationTest(unittest.TestCase):
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attention_mask = torch.tensor([1] * 6).reshape(1, -1)
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visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
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with torch.no_grad():
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output = model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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@@ -667,6 +670,7 @@ class VisualBertModelIntegrationTest(unittest.TestCase):
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visual_token_type_ids = torch.ones(size=(1, 4, 10), dtype=torch.long)
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visual_attention_mask = torch.ones_like(visual_token_type_ids)
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with torch.no_grad():
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output = model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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