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fix subtraction broadcasted access memory wrong values

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This commit is contained in:
lucasdelimanogueira 2024-05-18 09:01:42 -03:00
parent 1094a9c1cd
commit acb6c17cca
3 changed files with 15 additions and 14 deletions
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norch/__pycache__/tensor.cpython-38.pyc
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27
norch/tensor.py
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@ -223,8 +223,8 @@ class Tensor:
def __add__(self, other):
if isinstance(other, (int, float)):
other = other * self.ones_like()
broadcasted_shape = []
broadcasted_shape_add = []
# Function to determine if broadcasting is needed and get the broadcasted shape
def broadcast_shape(shape1, shape2):
@ -239,10 +239,10 @@ class Tensor:
for dim1, dim2 in zip(shape1, shape2):
if dim1 != dim2 and dim1 != 1 and dim2 != 1:
raise ValueError("Shapes are not compatible for broadcasting")
broadcasted_shape.append(max(dim1, dim2))
return broadcasted_shape, True
broadcasted_shape_add.append(max(dim1, dim2))
return broadcasted_shape_add, True
broadcasted_shape, needs_broadcasting = broadcast_shape(self.shape, other.shape)
broadcasted_shape_add, needs_broadcasting = broadcast_shape(self.shape, other.shape)
if needs_broadcasting:
# Call add_broadcasted_tensor if broadcasting is needed
@ -253,8 +253,8 @@ class Tensor:
result_data = Tensor()
result_data.tensor = result_tensor_ptr
result_data.shape = broadcasted_shape
result_data.ndim = len(broadcasted_shape)
result_data.shape = broadcasted_shape_add.copy()
result_data.ndim = len(broadcasted_shape_add)
result_data.device = self.device
result_data.numel = 1
@ -316,7 +316,7 @@ class Tensor:
if isinstance(other, (int, float)):
other = other * self.ones_like()
broadcasted_shape = []
broadcasted_shape_sub = []
# Function to determine if broadcasting is needed and get the broadcasted shape
def broadcast_shape(shape1, shape2):
@ -327,13 +327,14 @@ class Tensor:
shape1 = [1] * (max_len - len(shape1)) + shape1
shape2 = [1] * (max_len - len(shape2)) + shape2
for dim1, dim2 in zip(shape1, shape2):
if dim1 != dim2 and dim1 != 1 and dim2 != 1:
raise ValueError("Shapes are not compatible for broadcasting")
broadcasted_shape.append(max(dim1, dim2))
return broadcasted_shape, True
broadcasted_shape_sub.append(max(dim1, dim2))
return broadcasted_shape_sub, True
broadcasted_shape, needs_broadcasting = broadcast_shape(self.shape, other.shape)
broadcasted_shape_sub, needs_broadcasting = broadcast_shape(self.shape, other.shape)
if needs_broadcasting:
# Call add_broadcasted_tensor if broadcasting is needed
@ -344,8 +345,8 @@ class Tensor:
result_data = Tensor()
result_data.tensor = result_tensor_ptr
result_data.shape = broadcasted_shape
result_data.ndim = len(broadcasted_shape)
result_data.shape = broadcasted_shape_sub.copy()
result_data.ndim = len(broadcasted_shape_sub)
result_data.device = self.device
result_data.numel = self.numel # Update this to calculate the correct number of elements if broadcasting

2
test.py
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@ -9,7 +9,7 @@ W2 = norch.Tensor([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], requires_
B2 = norch.Tensor([[1, 2, 3, 4, 5]], requires_grad=True)
Z1 = W1 @ X - B1
Z1 = W1 @ X + B1
# Perform matrix multiplication