aparler/PyNorch
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

fix test autograd sub broadcasted

Browse source
This commit is contained in:
lucasdelimanogueira 2024-05-17 13:49:32 -03:00
parent de72cd67ba
commit abcd17e679
4 changed files with 9 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

BIN
norch/__pycache__/tensor.cpython-38.pyc
View file

Binary file not shown.

BIN
norch/autograd/__pycache__/functions.cpython-38.pyc
View file

Binary file not shown.

1
norch/autograd/functions.py
View file

@ -15,6 +15,7 @@ class AddBroadcastedBackward:
x, y = self.input
grad_x = self._reshape_gradient(gradient, x.shape)
grad_y = self._reshape_gradient(gradient, y.shape)
return [grad_x, grad_y]
def _reshape_gradient(self, gradient, shape):

8
tests/test_autograd.py
View file

@ -80,12 +80,17 @@ class TestTensorAutograd(unittest.TestCase):
self.assertTrue(utils.compare_torch(norch_tensor2_grad, torch_tensor2_grad))
## reversed order broadcasting
norch_tensor1 = norch.Tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True).to(self.device) # Shape (1, 2, 3)
norch_tensor2 = norch.Tensor([1.5, -1, 0], requires_grad=True).to(self.device) # Shape (3)
norch_result = (norch_tensor2 + norch_tensor1).sum()
norch_result.backward()
norch_tensor1_grad = utils.to_torch(norch_tensor1.grad).to(self.device)
norch_tensor2_grad = utils.to_torch(norch_tensor2.grad).to(self.device)
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True).to(self.device) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True).to(self.device) # Shape (3)
torch_result = (torch_tensor2 + torch_tensor1).sum()
torch_result.backward()
torch_tensor1_grad = torch_tensor1.grad
@ -137,6 +142,8 @@ class TestTensorAutograd(unittest.TestCase):
self.assertTrue(utils.compare_torch(norch_tensor2_grad, torch_tensor2_grad))
# reversed order broadcasting
norch_tensor1 = norch.Tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True).to(self.device) # Shape (1, 2, 3)
norch_tensor2 = norch.Tensor([1.5, -1, 0], requires_grad=True).to(self.device) # Shape (3)
norch_result = (norch_tensor2 - norch_tensor1).sum()
norch_result.backward()
@ -145,6 +152,7 @@ class TestTensorAutograd(unittest.TestCase):
torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True).to(self.device) # Shape (1, 2, 3)
torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True).to(self.device) # Shape (3)
torch_result = (torch_tensor2 - torch_tensor1).sum()
torch_result.backward()
torch_tensor1_grad = torch_tensor1.grad