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fix send to device unittest

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lucasdelimanogueira 2024-05-23 10:04:44 -03:00
parent b1c28c4cea
commit f13d22eb91
2 changed files with 19 additions and 48 deletions
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2
tests/test_autograd.py
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@ -980,7 +980,7 @@ class TestTensorAutograd(unittest.TestCase):
torch_tensor1 = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_tensor2 = torch.tensor([[1, 5.1], [0.1, -4], [0, 6], [7, 8]], dtype=torch.float32, requires_grad=True, device=self.device)
torch_result_transpose_matmul = (torch_tensor1.T @ torch_tensor2).sum()
torch_result_transpose_matmul.backward()
torch_tensor_grad_transpose_matmul1 = torch_tensor1.grad

65
tests/test_nn.py
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@ -26,11 +26,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([[1.1, 2, 3, 4], [1.1, 2, 3, 4]])
labels_torch = torch.tensor([[1.1, 2, 3, 4], [1.1, 2, 3, 3]])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[1.1, 2, 3, 4], [1.1, 2, 3, 4]], device=self.device)
labels_torch = torch.tensor([[1.1, 2, 3, 4], [1.1, 2, 3, 3]], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -42,11 +39,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([1.1, 2, 3, 4])
labels_torch = torch.tensor([4, 3, 2.1, 1])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([1.1, 2, 3, 4], device=self.device)
labels_torch = torch.tensor([4, 3, 2.1, 1], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -66,11 +60,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([2.0, 1.0, 0.1])
labels_torch = torch.tensor(0)
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([2.0, 1.0, 0.1], device=self.device)
labels_torch = torch.tensor(0, device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -83,11 +74,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([[0.5, 1.5, 2.5], [1.0, 2.0, 3.0]])
labels_torch = torch.tensor([2, 1])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[0.5, 1.5, 2.5], [1.0, 2.0, 3.0]], device=self.device)
labels_torch = torch.tensor([2, 1], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -99,11 +87,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
labels_torch = torch.tensor([1, 2])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], device=self.device)
labels_torch = torch.tensor([1, 2], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -131,11 +116,8 @@ class TestNNModuleLoss(unittest.TestCase):
loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
loss_torch_result = utils.to_torch(loss_norch).to(self.device)
predictions_torch = torch.tensor([[0.5, 0.2, 0.1], [0.1, 0.5, 0.7]])
labels_torch = torch.tensor([[1., 0, 0], [0, 1, 0]])
predictions_torch.to(self.device)
labels_torch.to(self.device)
predictions_torch = torch.tensor([[0.5, 0.2, 0.1], [0.1, 0.5, 0.7]], device=self.device)
labels_torch = torch.tensor([[1., 0, 0], [0, 1, 0]], device=self.device)
loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
@ -161,9 +143,7 @@ class TestNNModuleActivationFn(unittest.TestCase):
sigmoid_norch = sigmoid_fn_norch.forward(x)
sigmoid_torch_result = utils.to_torch(sigmoid_norch).to(self.device)
x = torch.tensor([[1, 2, 3]])
x.to(self.device)
x = torch.tensor([[1, 2, 3]], device=self.device)
sigmoid_torch_expected = sigmoid_fn_torch.forward(x)
@ -174,9 +154,7 @@ class TestNNModuleActivationFn(unittest.TestCase):
sigmoid_norch = sigmoid_fn_norch.forward(x)
sigmoid_torch_result = utils.to_torch(sigmoid_norch).to(self.device)
x = torch.tensor([-1, 2, -3])
x.to(self.device)
x = torch.tensor([-1, 2, -3], device=self.device)
sigmoid_torch_expected = sigmoid_fn_torch.forward(x)
@ -187,9 +165,7 @@ class TestNNModuleActivationFn(unittest.TestCase):
sigmoid_norch = sigmoid_fn_norch.forward(x)
sigmoid_torch_result = utils.to_torch(sigmoid_norch).to(self.device)
x = torch.tensor([0, 0, 0])
x.to(self.device)
x = torch.tensor([0, 0, 0], device=self.device)
sigmoid_torch_expected = sigmoid_fn_torch.forward(x)
@ -205,9 +181,9 @@ class TestNNModuleActivationFn(unittest.TestCase):
# Define the input tensors for different test cases
test_cases = [
(norch.Tensor([[[1., 2, 3], [4, 5, 6]]]), torch.tensor([[[1., 2, 3], [4, 5, 6]]])),
(norch.Tensor([[[1., -1, 0], [2, -2, 0]]]), torch.tensor([[[1., -1, 0], [2, -2, 0]]])),
(norch.Tensor([[[0., 0, 0], [0, 0, 0]]]), torch.tensor([[[0., 0, 0], [0, 0, 0]]]))
(norch.Tensor([[[1., 2, 3], [4, 5, 6]]]).to(self.device), torch.tensor([[[1., 2, 3], [4, 5, 6]]], device=self.device)),
(norch.Tensor([[[1., -1, 0], [2, -2, 0]]]).to(self.device), torch.tensor([[[1., -1, 0], [2, -2, 0]]], device=self.device)),
(norch.Tensor([[[0., 0, 0], [0, 0, 0]]]).to(self.device), torch.tensor([[[0., 0, 0], [0, 0, 0]]], device=self.device))
]
for dim in axes:
@ -215,12 +191,7 @@ class TestNNModuleActivationFn(unittest.TestCase):
softmax_fn_torch = torch.nn.Softmax(dim=dim)
for norch_input, torch_input in test_cases:
# Move tensors to the correct device
norch_input = norch_input.to(self.device)
torch_input = torch_input
torch_input.to(self.device)
# Forward pass using norch
softmax_norch = softmax_fn_norch.forward(norch_input)
softmax_torch_result = utils.to_torch(softmax_norch).to(self.device)