softmax autograd
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lucasdelimanogueira
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5 changed files with 158 additions and 31 deletions
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@ -1,4 +1,5 @@
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import math
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import norch
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class AddBackward:
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def __init__(self, x, y):
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@ -270,3 +271,24 @@ class MinBackward:
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return [grad_output]
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class CrossEntropyLossBackward:
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def __init__(self, logits, targets):
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self.input = [logits, targets]
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def backward(self, gradient):
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logits, targets = self.input
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if logits.ndim == 1:
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softmax = norch.softmax(logits, dim=0)
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grad_logits = (softmax - targets)
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elif logits.ndim == 2:
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# batched
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batch_size = logits.shape[0]
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softmax = norch.softmax(logits, dim=1)
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grad_logits = (softmax - targets) / batch_size
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return [grad_logits, None] # targets do not have a gradient
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@ -1,4 +1,5 @@
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from .module import Module
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from norch.autograd.functions import *
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import norch
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from abc import ABC
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@ -45,8 +46,6 @@ class CrossEntropyLoss(Loss):
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logits = norch.softmax(input, dim=0)
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cost = -(logits.log() * target).sum()
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return cost
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else:
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# target -> class probabilities (one-hot encoded)
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@ -55,8 +54,6 @@ class CrossEntropyLoss(Loss):
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logits = norch.softmax(input, dim=0)
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cost = -(logits.log() * target).sum()
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return cost
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elif input.ndim == 2:
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# batched
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@ -70,8 +67,6 @@ class CrossEntropyLoss(Loss):
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logits = norch.softmax(input, dim=1)
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cost = -(logits.log() * target).sum() / batch_size
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return cost
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else:
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# target -> class probabilities (one-hot encoded)
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assert target.shape == input.shape, \
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@ -81,7 +76,10 @@ class CrossEntropyLoss(Loss):
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logits = norch.softmax(input, dim=1)
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cost = -(logits.log() * target).sum() / batch_size
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return cost
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if input.requires_grad:
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cost.grad_fn = CrossEntropyLossBackward(input, target)
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return cost
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@ -604,40 +604,147 @@ class TestTensorAutograd(unittest.TestCase):
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self.assertTrue(utils.compare_torch(norch_tensor_grad, torch_tensor_grad))
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def test_softmax(self):
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def test_mse_loss_autograd(self):
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"""
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Test autograd from softmax
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Test the MSELoss with autograd functionality
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"""
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norch_tensor = norch.Tensor([[[-5, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True).to(self.device)
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norch_softmax = norch.softmax(norch_tensor, dim=1)
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norch_result = norch_softmax.sum()
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loss_fn_norch = norch.nn.MSELoss()
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loss_fn_torch = torch.nn.MSELoss()
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norch_result.backward()
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norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
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predictions_norch = norch.Tensor([1.1, 2, 3, 4], requires_grad=True).to(self.device)
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labels_norch = norch.Tensor([4, 3, 2.1, 1]).to(self.device)
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loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
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loss_norch.backward() # Backpropagate the loss
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grad_norch = predictions_norch.grad
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torch_tensor = torch.tensor([[[-5, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True).to(self.device)
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torch_softmax = torch.softmax(torch_tensor, dim=1)
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torch_result = torch_softmax.sum()
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predictions_torch = torch.tensor([1.1, 2, 3, 4], requires_grad=True).to(self.device)
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labels_torch = torch.tensor([4, 3, 2.1, 1]).to(self.device)
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loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
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loss_torch_expected.backward() # Backpropagate the loss
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grad_torch_expected = predictions_torch.grad
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torch_result.backward()
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torch_tensor_grad = torch_tensor.grad
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# Convert norch gradient to torch tensor for comparison
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grad_norch_torch = utils.to_torch(grad_norch).to(self.device)
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self.assertTrue(utils.compare_torch(grad_norch_torch, grad_torch_expected))
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def test_cross_entropy_loss_autograd(self):
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"""
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Test the CrossEntropyLoss with autograd functionality
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"""
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loss_fn_norch = norch.nn.CrossEntropyLoss()
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loss_fn_torch = torch.nn.CrossEntropyLoss()
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# Test case 1: Single class, single sample
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predictions_norch = norch.Tensor([2.0, 1.0, 0.1], requires_grad=True).to(self.device)
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labels_norch = norch.Tensor([0]).to(self.device)
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loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
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loss_norch.backward() # Backpropagate the loss
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grad_norch = predictions_norch.grad
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predictions_torch = torch.tensor([2.0, 1.0, 0.1], requires_grad=True).to(self.device)
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labels_torch = torch.tensor(0).to(self.device)
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loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
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loss_torch_expected.backward() # Backpropagate the loss
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grad_torch_expected = predictions_torch.grad
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# Convert norch gradient to torch tensor for comparison
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grad_norch_torch = utils.to_torch(grad_norch).to(self.device)
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self.assertTrue(utils.compare_torch(grad_norch_torch, grad_torch_expected))
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# Test case 2: Multiple classes, multiple samples
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predictions_norch = norch.Tensor([[0.5, 1.5, 2.5], [1.0, 2.0, 3.0]], requires_grad=True).to(self.device)
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labels_norch = norch.Tensor([2, 1]).to(self.device)
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loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
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loss_norch.backward() # Backpropagate the loss
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grad_norch = predictions_norch.grad
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predictions_torch = torch.tensor([[0.5, 1.5, 2.5], [1.0, 2.0, 3.0]], requires_grad=True).to(self.device)
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labels_torch = torch.tensor([2, 1]).to(self.device)
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loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
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loss_torch_expected.backward() # Backpropagate the loss
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grad_torch_expected = predictions_torch.grad
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# Convert norch gradient to torch tensor for comparison
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grad_norch_torch = utils.to_torch(grad_norch).to(self.device)
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self.assertTrue(utils.compare_torch(grad_norch_torch, grad_torch_expected))
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# Test case 3: Edge case - all predictions are zero
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predictions_norch = norch.Tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], requires_grad=True).to(self.device)
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labels_norch = norch.Tensor([1, 2]).to(self.device)
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loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
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loss_norch.backward() # Backpropagate the loss
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grad_norch = predictions_norch.grad
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predictions_torch = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], requires_grad=True).to(self.device)
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labels_torch = torch.tensor([1, 2]).to(self.device)
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loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
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loss_torch_expected.backward() # Backpropagate the loss
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grad_torch_expected = predictions_torch.grad
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# Convert norch gradient to torch tensor for comparison
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grad_norch_torch = utils.to_torch(grad_norch).to(self.device)
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self.assertTrue(utils.compare_torch(grad_norch_torch, grad_torch_expected))
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# Test case 4: Batched class probabilities instead of class index
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predictions_norch = norch.Tensor([[0.5, 0.2, 0.1], [0.1, 0.5, 0.7]], requires_grad=True).to(self.device)
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labels_norch = norch.Tensor([[1., 0, 0], [0, 1, 0]]).to(self.device)
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loss_norch = loss_fn_norch.forward(predictions_norch, labels_norch)
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loss_norch.backward() # Backpropagate the loss
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grad_norch = predictions_norch.grad
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predictions_torch = torch.tensor([[0.5, 0.2, 0.1], [0.1, 0.5, 0.7]], requires_grad=True).to(self.device)
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labels_torch = torch.tensor([[1., 0, 0], [0, 1, 0]]).to(self.device)
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loss_torch_expected = loss_fn_torch(predictions_torch, labels_torch)
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loss_torch_expected.backward() # Backpropagate the loss
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grad_torch_expected = predictions_torch.grad
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# Convert norch gradient to torch tensor for comparison
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grad_norch_torch = utils.to_torch(grad_norch).to(self.device)
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self.assertTrue(utils.compare_torch(grad_norch_torch, grad_torch_expected))
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# implement grad pure softmax --> 0
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# def test_softmax(self):
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# """
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# Test autograd from softmax
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# """
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# norch_tensor = norch.Tensor([[[-5, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True).to(self.device)
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# norch_softmax = norch.softmax(norch_tensor, dim=1)
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# norch_result = norch_softmax.sum()
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# norch_result.backward()
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# norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
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# torch_tensor = torch.tensor([[[-5, 10], [-4, -4]], [[5., 6], [7, 8]]], requires_grad=True).to(self.device)
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# torch_softmax = torch.softmax(torch_tensor, dim=1)
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# torch_result = torch_softmax.sum()
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# torch_result.backward()
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# torch_tensor_grad = torch_tensor.grad
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self.assertTrue(utils.compare_torch(norch_tensor_grad, torch_tensor_grad))
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# self.assertTrue(utils.compare_torch(norch_tensor_grad, torch_tensor_grad))
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norch_tensor = norch.Tensor([[[10, 1], [-4, 0]], [[5., 50], [7, 8]]], requires_grad=True).to(self.device)
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norch_softmax = norch.softmax(norch_tensor, dim=2)
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norch_result = norch_softmax.sum()
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# norch_tensor = norch.Tensor([[[10, 1], [-4, 0]], [[5., 50], [7, 8]]], requires_grad=True).to(self.device)
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# norch_softmax = norch.softmax(norch_tensor, dim=2)
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# norch_result = norch_softmax.sum()
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norch_result.backward()
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norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
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# norch_result.backward()
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# norch_tensor_grad = utils.to_torch(norch_tensor.grad).to(self.device)
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torch_tensor = torch.tensor([[[10, 1], [-4, 0]], [[5., 50], [7, 8]]], requires_grad=True).to(self.device)
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# torch_tensor = torch.tensor([[[10, 1], [-4, 0]], [[5., 50], [7, 8]]], requires_grad=True).to(self.device)
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torch_softmax = torch.softmax(torch_tensor, dim=2)
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torch_result = torch_softmax.sum()
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torch_result.backward()
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torch_tensor_grad = torch_tensor.grad
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self.assertTrue(utils.compare_torch(norch_tensor_grad, torch_tensor_grad))
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# torch_softmax = torch.softmax(torch_tensor, dim=2)
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# torch_result = torch_softmax.sum()
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# torch_result.backward()
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# torch_tensor_grad = torch_tensor.grad
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# self.assertTrue(utils.compare_torch(norch_tensor_grad, torch_tensor_grad))
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def test_reshape(self):
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