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wrap forward passes with torch.no_grad() (#19416)

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Partho
2022-10-11 00:33:09 +05:30
committed by GitHub
parent 870a9542be
commit d739a707d9
1 changed files with 22 additions and 15 deletions
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7
tests/models/tapas/test_modeling_tapas.py
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@@ -570,6 +570,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
table, queries = prepare_tapas_single_inputs_for_inference() table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, return_tensors="pt") inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()} inputs = {k: v.to(torch_device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs) outputs = model(**inputs)
# test the sequence output # test the sequence output
expected_slice = torch.tensor( expected_slice = torch.tensor(
@@ -608,6 +609,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
table, queries = prepare_tapas_single_inputs_for_inference() table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, return_tensors="pt") inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()} inputs = {k: v.to(torch_device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs) outputs = model(**inputs)
# test the logits # test the logits
logits = outputs.logits logits = outputs.logits
@@ -657,6 +659,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
table, queries = prepare_tapas_single_inputs_for_inference() table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, return_tensors="pt") inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()} inputs = {k: v.to(torch_device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs) outputs = model(**inputs)
# test the logits # test the logits
logits = outputs.logits logits = outputs.logits
@@ -705,6 +708,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt") inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt")
inputs_on_device = {k: v.to(torch_device) for k, v in inputs.items()} inputs_on_device = {k: v.to(torch_device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs_on_device) outputs = model(**inputs_on_device)
# test the logits # test the logits
logits = outputs.logits logits = outputs.logits
@@ -774,6 +778,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
float_answer = torch.FloatTensor(float_answer).to(torch_device) float_answer = torch.FloatTensor(float_answer).to(torch_device)
# forward pass to get loss + logits: # forward pass to get loss + logits:
with torch.no_grad():
outputs = model( outputs = model(
input_ids=input_ids, input_ids=input_ids,
attention_mask=attention_mask, attention_mask=attention_mask,
@@ -829,6 +834,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
table, queries = prepare_tapas_single_inputs_for_inference() table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, return_tensors="pt") inputs = tokenizer(table=table, queries=queries, return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()} inputs = {k: v.to(torch_device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs) outputs = model(**inputs)
# test the logits # test the logits
logits = outputs.logits logits = outputs.logits
@@ -884,6 +890,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
table, queries = prepare_tapas_single_inputs_for_inference() table, queries = prepare_tapas_single_inputs_for_inference()
inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt") inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt")
inputs = {k: v.to(torch_device) for k, v in inputs.items()} inputs = {k: v.to(torch_device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs) outputs = model(**inputs)
# test the classification logits # test the classification logits