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softmax operation

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lucasdelimanogueira 2024-05-21 01:22:38 -03:00
parent fd831f950d
commit a39bd59a3f
9 changed files with 67 additions and 21 deletions
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build/tensor.o
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norch/__pycache__/tensor.cpython-38.pyc
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4
norch/csrc/tensor.cpp
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@ -639,7 +639,7 @@ extern "C" {
Tensor* tensor_div_tensor(Tensor* tensor1, Tensor* tensor2) {
if (tensor1->ndim != tensor2->ndim) {
fprintf(stderr, "Tensors must have the same number of dimensions %d and %d for element-wise multiplication\n", tensor1->ndim, tensor2->ndim);
fprintf(stderr, "Tensors must have the same number of dimensions %d and %d for element-wise division\n", tensor1->ndim, tensor2->ndim);
exit(1);
}
@ -664,7 +664,7 @@ extern "C" {
for (int i = 0; i < ndim; i++) {
if (tensor1->shape[i] != tensor2->shape[i]) {
fprintf(stderr, "Tensors must have the same shape %d and %d at index %d for subtraction\n", tensor1->shape[i], tensor2->shape[i], i);
fprintf(stderr, "Tensors must have the same shape %d and %d at index %d for division\n", tensor1->shape[i], tensor2->shape[i], i);
exit(1);
}
shape[i] = tensor1->shape[i];

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norch/libtensor.so
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22
norch/nn/functional.py
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@ -4,15 +4,15 @@ def sigmoid(x):
return 1.0 / (1.0 + (math.e) ** (-x))
def softmax(x, dim=None):
e = math.e ** x
s = e.sum(axis=dim, keepdim=True)
#l = s.log()
print('x', x)
print('\n\n')
#print('log', l)
if dim is not None and dim < 0:
dim = x.ndim + dim
x_max = x.max(axis=dim, keepdim=True)
exp_x = math.e ** (x - x_max)
print('\n\n')
#print('x-log', x - l)
#return math.e ** (x - sum.log())
if dim is not None:
sum_exp_x = exp_x.sum(axis=dim, keepdim=True) + exp_x.zeros_like()
return exp_x / sum_exp_x
else:
sum_exp_x = exp_x.sum()
return exp_x / sum_exp_x

16
norch/tensor.py
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@ -613,6 +613,9 @@ class Tensor:
result_data.grad_fn = DivisionBackward(self, other)
elif isinstance(self, Tensor) and isinstance(other, Tensor):
if other.numel == 1:
return self.__truediv__(other.tensor.contents.data[0])
Tensor._C.tensor_div_tensor.argtypes = [ctypes.POINTER(CTensor), ctypes.POINTER(CTensor)]
Tensor._C.tensor_div_tensor.restype = ctypes.POINTER(CTensor)
@ -749,8 +752,12 @@ class Tensor:
return result_data
def sum(self, axis=None, keepdim=False):
if axis is not None and axis < 0:
axis = self.ndim + axis
if axis == None:
axis = -1
Tensor._C.sum_tensor.argtypes = [ctypes.POINTER(CTensor), ctypes.c_int, ctypes.c_bool]
Tensor._C.sum_tensor.restype = ctypes.POINTER(CTensor)
@ -786,12 +793,15 @@ class Tensor:
return result_data
def max(self, axis=None, keepdim=False):
if axis is not None and axis < 0:
axis = self.ndim + axis
if axis == None:
axis = -1
Tensor._C.max_tensor.argtypes = [ctypes.POINTER(CTensor), ctypes.c_int, ctypes.c_bool]
Tensor._C.max_tensor.restype = ctypes.POINTER(CTensor)
print(axis, keepdim)
result_tensor_ptr = Tensor._C.max_tensor(self.tensor, axis, keepdim)
result_data = Tensor()
@ -824,8 +834,12 @@ class Tensor:
return result_data
def min(self, axis=None, keepdim=False):
if axis is not None and axis < 0:
axis = self.ndim + axis
if axis == None:
axis = -1
Tensor._C.min_tensor.argtypes = [ctypes.POINTER(CTensor), ctypes.c_int, ctypes.c_bool]
Tensor._C.min_tensor.restype = ctypes.POINTER(CTensor)

3
test.py
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@ -7,8 +7,9 @@ import random
random.seed(1)
torch_tensor = norch.Tensor([[[2, 2], [-1, -1]], [[1., 2], [3, 3]]], requires_grad=True)#.to(self.device)
torch_tensor = norch.Tensor([[[2, 2], [2, 2]], [[2, 2.], [2, 2]]])
b = norch.nn.functional.softmax(torch_tensor)
print(b)
"""a = norch.Tensor([[[4.186502456665039]]])
b = norch.Tensor([[[2.0, 2.0,],[-1.0, -1.0,]],[[1.0, 2.0,],[3.0, 3.0,]]])

10
tests/test_nn.py
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@ -95,13 +95,13 @@ class TestNNModuleActivationFn(unittest.TestCase):
"""
# Test different axes
axes = [None, 0, 1, -1]
axes = [0, 1, 2, -1]
# 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]]]), 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]]]))
]
for dim in axes:
@ -121,7 +121,5 @@ class TestNNModuleActivationFn(unittest.TestCase):
softmax_torch_expected = softmax_fn_torch.forward(torch_input)
# Compare the results
print(softmax_torch_result)
print(softmax_torch_expected)
self.assertTrue(utils.compare_torch(softmax_torch_result, softmax_torch_expected))

33
tests/test_operations.py
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@ -260,6 +260,17 @@ class TestTensorOperations(unittest.TestCase):
self.assertTrue(utils.compare_torch(torch_result, torch_expected))
# negative axis
norch_tensor = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]).to(self.device)
norch_result = norch_tensor.sum(axis=-2)
torch_result = utils.to_torch(norch_result).to(self.device)
torch_tensor = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]).to(self.device)
torch_expected = torch.sum(torch_tensor, dim=-2)
self.assertTrue(utils.compare_torch(torch_result, torch_expected))
def test_sum_axis_keepdim(self):
"""
@ -301,6 +312,17 @@ class TestTensorOperations(unittest.TestCase):
self.assertTrue(utils.compare_torch(torch_result, torch_expected))
# negative axis
norch_tensor = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]).to(self.device)
norch_result = norch_tensor.max(axis=-1)
torch_result = utils.to_torch(norch_result).to(self.device)
torch_tensor = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]).to(self.device)
torch_expected, _ = torch.max(torch_tensor, dim=-1)
self.assertTrue(utils.compare_torch(torch_result, torch_expected))
def test_max_axis_keepdim(self):
"""
@ -341,6 +363,17 @@ class TestTensorOperations(unittest.TestCase):
self.assertTrue(utils.compare_torch(torch_result, torch_expected))
# negative axis
norch_tensor = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]).to(self.device)
norch_result = norch_tensor.min(axis=-1)
torch_result = utils.to_torch(norch_result).to(self.device)
torch_tensor = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]).to(self.device)
torch_expected, _ = torch.min(torch_tensor, dim=-1)
self.assertTrue(utils.compare_torch(torch_result, torch_expected))
def test_min_axis_keepdim(self):
"""