diff --git a/norch/__pycache__/tensor.cpython-38.pyc b/norch/__pycache__/tensor.cpython-38.pyc index ce87ffb..62b2210 100644 Binary files a/norch/__pycache__/tensor.cpython-38.pyc and b/norch/__pycache__/tensor.cpython-38.pyc differ diff --git a/norch/nn/__pycache__/module.cpython-38.pyc b/norch/nn/__pycache__/module.cpython-38.pyc index a05955b..455e790 100644 Binary files a/norch/nn/__pycache__/module.cpython-38.pyc and b/norch/nn/__pycache__/module.cpython-38.pyc differ diff --git a/norch/nn/module.py b/norch/nn/module.py index 9e041d0..d0ffaf1 100644 --- a/norch/nn/module.py +++ b/norch/nn/module.py @@ -51,9 +51,11 @@ class Module(ABC): parameter.zero_grad() def to(self, device): - for parameter in self.parameters(): + for _, _, parameter in self.parameters(): parameter.to(device) + return self + def state_dict(self): state = OrderedDict() for i, param in enumerate(self.parameters()): diff --git a/norch/optim/optimizers/__pycache__/sgd.cpython-38.pyc b/norch/optim/optimizers/__pycache__/sgd.cpython-38.pyc index 6349839..869d1b1 100644 Binary files a/norch/optim/optimizers/__pycache__/sgd.cpython-38.pyc and b/norch/optim/optimizers/__pycache__/sgd.cpython-38.pyc differ diff --git a/norch/tensor.py b/norch/tensor.py index 27ac305..ac0d996 100644 --- a/norch/tensor.py +++ b/norch/tensor.py @@ -137,27 +137,36 @@ class Tensor: def backward(self, gradient=None): if not self.requires_grad: return - + if gradient is None: if self.shape == [1]: gradient = Tensor([1]) else: raise RuntimeError("Gradient argument must be specified for non-scalar tensors.") - if self.grad is None: - self.grad = gradient + stack = [(self, gradient)] + visited = set() + + while stack: + tensor, grad = stack.pop() + + if tensor.grad is None: + tensor.grad = grad + else: + tensor.grad += grad - else: - self.grad += gradient - - if self.grad_fn is not None: # not a leaf - grads = self.grad_fn.backward(gradient) - for tensor, grad in zip(self.grad_fn.input, grads): - if isinstance(tensor, Tensor): - tensor.backward(grad) + # Propagate gradients to inputs if not a leaf tensor + if tensor.grad_fn is not None: + grads = tensor.grad_fn.backward(grad) + for tensor, grad in zip(tensor.grad_fn.input, grads): + if isinstance(tensor, Tensor) and tensor not in visited: + stack.append((tensor, grad)) + visited.add(tensor) def zero_grad(self): - self.grad = None + tmp = self.zeros_like() + self.detach() + self.grad = tmp def __getitem__(self, indices): if len(indices) != self.ndim: @@ -570,5 +579,13 @@ class Tensor: result_data.shape = self.shape.copy()[::-1] result_data.ndim = self.ndim result_data.device = self.device + + result_data.requires_grad = self.requires_grad - return result_data \ No newline at end of file + return result_data + + def detach(self): + self.grad = None + self.grad_fn = None + + return self \ No newline at end of file diff --git a/profile.txt b/profile.txt new file mode 100644 index 0000000..6565bd8 --- /dev/null +++ b/profile.txt @@ 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1 0.000 0.000 0.000 0.000 _iotools.py:437(ConversionWarning) + 1 0.000 0.000 0.000 0.000 {method '__exit__' of '_thread.lock' objects} + 1 0.000 0.000 0.000 0.000 {method '__enter__' of '_thread.lock' objects} + + diff --git a/test.py b/test.py index 2c3ee26..0b30999 100644 --- a/test.py +++ b/test.py @@ -19,6 +19,7 @@ if __name__ == "__main__": import time import random import numpy as np + import psutil """a = norch.Tensor([ [[1.234, 2.123], [3.635, 4.456], [5.678, 6.789]], @@ -69,11 +70,17 @@ if __name__ == "__main__": import norch.nn as nn + cpu_percent = psutil.cpu_percent(interval=1) + print(f"CPU Usage: {cpu_percent}%") + memory_usage = psutil.virtual_memory() + print(f"Memory Usage: {memory_usage.percent}%") + + class MeuModulo(nn.Module): def __init__(self): super(MeuModulo, self).__init__() - self.layer1 = nn.Linear(10, 2) + self.layer1 = nn.Linear(5, 2) #self.layer2 = nn.Linear(5, 2) self.sigmoid = nn.Sigmoid() @@ -86,33 +93,26 @@ if __name__ == "__main__": return out modelo = MeuModulo() - input_list = [[0.05 for _ in range(10)]] + input_list = [[0.05 for _ in range(5)]] input = norch.Tensor(input_list).T criterion = nn.MSELoss() - optimizer = norch.optim.SGD(modelo.parameters(), lr=0.5) + optimizer = norch.optim.SGD(modelo.parameters(), lr=0.1) target_list = [[0.1 for _ in range(2)]] target = norch.Tensor(target_list).T - for epoch in range(5): + for epoch in range(50): + cpu_percent = psutil.cpu_percent(interval=1) + memory_usage = psutil.virtual_memory() output = modelo(input) loss = criterion(output, target) optimizer.zero_grad() - #print('FORA ANTES: ', modelo.layer1.bias, '\n') - #print(modelo.layer1.weight.grad) loss.backward() - #print(modelo.layer1.weight.grad) optimizer.step() - #print('FORA DEPOIS: ', modelo.layer1.bias, '\n') - #print("\n\n") - #print(modelo.layer1.weight.grad) - - #print(modelo.layer1.weight[0,0]) print(loss) - exit() #### testar transpose axes!!!! make it contiguous @@ -122,15 +122,20 @@ if __name__ == "__main__": [[19, 20], [21, 22], [23, 24]], [[25, 26], [27, 28], [29, 0.030]]], requires_grad=True) - op = nn.Sigmoid() - result = op(tensor1) - result = result.sum() + #op = nn.Sigmoid() + tensor2 = + result = tensor1.sum() + result.backward() print(tensor1.grad) exit()""" # Reshape tensor1 to 2x3x5 - reshaped_tensor = tensor1.transpose(1, 0) + """tensor1 = norch.Tensor([[[1, 2], [3, 4], [5, 6]], + [[7, 8], [9, 10], [11, 12]], + [[13, 14], [15, 16], [17, 18]], + [[19, 20], [21, 22], [23, 24]], + [[25, 26], [27, 28], [29, 0.030]]], requires_grad=True) # Create a 5x4 tensor tensor2 = norch.Tensor([[1, 2, 3], @@ -138,16 +143,14 @@ if __name__ == "__main__": [9, 10, 11], [13, 14, 15], [17, 18, 19]]) - - tensor2 = tensor2.transpose(1,0) # Multiply reshaped_tensor by tensor2 - result = tensor2 @ reshaped_tensor + result = tensor2 @ tensor1 result = result.sum() result.backward() - print(tensor1.grad) + print(tensor1.grad)""" #print(a.shape, b.shape, result.shape) #c = result.sum()