wrap forward passes with torch.no_grad() (#19273)
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@@ -627,7 +627,8 @@ class BigBirdModelIntegrationTest(unittest.TestCase):
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model.to(torch_device)
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input_ids = torch.tensor([[20920, 232, 328, 1437] * 1024], dtype=torch.long, device=torch_device)
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outputs = model(input_ids)
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with torch.no_grad():
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outputs = model(input_ids)
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prediction_logits = outputs.prediction_logits
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seq_relationship_logits = outputs.seq_relationship_logits
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@@ -655,7 +656,8 @@ class BigBirdModelIntegrationTest(unittest.TestCase):
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model.to(torch_device)
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input_ids = torch.tensor([[20920, 232, 328, 1437] * 512], dtype=torch.long, device=torch_device)
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outputs = model(input_ids)
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with torch.no_grad():
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outputs = model(input_ids)
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prediction_logits = outputs.prediction_logits
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seq_relationship_logits = outputs.seq_relationship_logits
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@@ -920,7 +922,8 @@ class BigBirdModelIntegrationTest(unittest.TestCase):
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model.eval()
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input_ids = torch.tensor([200 * [10] + 40 * [2] + [1]], device=torch_device, dtype=torch.long)
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output = model(input_ids).to_tuple()[0]
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with torch.no_grad():
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output = model(input_ids).to_tuple()[0]
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# fmt: off
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target = torch.tensor(
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