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unit test autograd transpose, t and reshape then matmul

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lucasdelimanogueira 2024-05-07 13:39:18 -03:00
parent 708fc121d6
commit c5a776f43d
3 changed files with 83 additions and 44 deletions
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norch/__pycache__/tensor.cpython-38.pyc
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tests/test_autograd.py
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@ -2,9 +2,12 @@ import unittest
import norch
from norch import utils
import torch
import os
class TestTensorAutograd(unittest.TestCase):
def setUp(self):
self.device = os.environ.get('device', 'cpu')
def test_addition(self):
"""
Test autograd from addition two tensors: tensor1 + tensor2
@ -248,61 +251,68 @@ class TestTensorAutograd(unittest.TestCase):
norch_tensor_grad_T = utils.to_torch(norch_tensor_T.grad)
torch_tensor_T = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
torch_result_T = torch_tensor_T.T.sum()
torch_result_T = torch_tensor_T.mT.sum()
torch_result_T.backward()
torch_tensor_grad_T = torch_tensor_T.grad
self.assertTrue(utils.compare_torch(norch_tensor_grad_T, torch_tensor_grad_T))
def test_reshape_then_matmul(self):
"""
Test autograd from reshaping a tensor then performing matrix multiplication: matmul(tensor1.reshape(shape), tensor2)
"""
norch_tensor_reshape_matmul = norch.Tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
new_shape = (2, 4)
norch_result_reshape_matmul = norch.matmul(norch_tensor_reshape_matmul.reshape(new_shape), norch_tensor_reshape_matmul).sum()
norch_result_reshape_matmul.backward()
norch_tensor_grad_reshape_matmul = utils.to_torch(norch_tensor_reshape_matmul.grad)
def test_reshape_then_matmul(self):
"""
Test autograd from reshaping a tensor then performing matrix multiplication: matmul(tensor1.reshape(shape), tensor2)
"""
norch_tensor_reshape_matmul = norch.Tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], requires_grad=True)
new_shape = [2, 4]
norch_result_reshape_matmul = (norch_tensor_reshape_matmul.reshape(new_shape) @ norch_tensor_reshape_matmul).sum()
norch_result_reshape_matmul.backward()
norch_tensor_grad_reshape_matmul = utils.to_torch(norch_tensor_reshape_matmul.grad)
torch_tensor_reshape_matmul = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], dtype=torch.float32, requires_grad=True)
torch_result_reshape_matmul = torch.matmul(torch_tensor_reshape_matmul.reshape(new_shape), torch_tensor_reshape_matmul).sum()
torch_result_reshape_matmul.backward()
torch_tensor_grad_reshape_matmul = torch_tensor_reshape_matmul.grad
self.assertTrue(utils.compare_torch(norch_tensor_grad_reshape_matmul, torch_tensor_grad_reshape_matmul))
torch_tensor_reshape_matmul = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True)
torch_result_reshape_matmul = torch.matmul(torch_tensor_reshape_matmul.reshape(new_shape), torch_tensor_reshape_matmul).sum()
torch_result_reshape_matmul.backward()
torch_tensor_grad_reshape_matmul = torch_tensor_reshape_matmul.grad
print(norch_tensor_grad_reshape_matmul)
print(torch_tensor_grad_reshape_matmul)
print("\n\n\n\n@@")
self.assertTrue(utils.compare_torch(norch_tensor_grad_reshape_matmul, torch_tensor_grad_reshape_matmul))
def test_T_then_matmul(self):
"""
Test autograd from transposing a tensor then performing matrix multiplication: matmul(tensor.T, tensor)
"""
norch_tensor_T_matmul = norch.Tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
norch_result_T_matmul = norch.matmul(norch_tensor_T_matmul.T, norch_tensor_T_matmul).sum()
norch_result_T_matmul.backward()
norch_tensor_grad_T_matmul = utils.to_torch(norch_tensor_T_matmul.grad)
def test_T_then_matmul(self):
"""
Test autograd from transposing a tensor then performing matrix multiplication: matmul(tensor.T, tensor)
"""
norch_tensor_T_matmul = norch.Tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], requires_grad=True)
norch_result_T_matmul = (norch_tensor_T_matmul.T @ norch_tensor_T_matmul).sum()
norch_result_T_matmul.backward()
norch_tensor_grad_T_matmul = utils.to_torch(norch_tensor_T_matmul.grad)
torch_tensor_T_matmul = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], dtype=torch.float32, requires_grad=True)
torch_result_T_matmul = torch.matmul(torch_tensor_T_matmul.T, torch_tensor_T_matmul).sum()
torch_result_T_matmul.backward()
torch_tensor_grad_T_matmul = torch_tensor_T_matmul.grad
torch_tensor_T_matmul = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True)
torch_result_T_matmul = torch.matmul(torch_tensor_T_matmul.T, torch_tensor_T_matmul).sum()
torch_result_T_matmul.backward()
torch_tensor_grad_T_matmul = torch_tensor_T_matmul.grad
self.assertTrue(utils.compare_torch(norch_tensor_grad_T_matmul, torch_tensor_grad_T_matmul))
self.assertTrue(utils.compare_torch(norch_tensor_grad_T_matmul, torch_tensor_grad_T_matmul))
def test_transpose_axes_then_matmul(self):
"""
Test autograd from transposing a tensor with specific axes then performing matrix multiplication: matmul(tensor.transpose(axis1, axis2), tensor)
"""
norch_tensor_transpose_matmul = norch.Tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], requires_grad=True)
axis1, axis2 = 0, 2
norch_result_transpose_matmul = norch.matmul(norch_tensor_transpose_matmul.transpose(axis1, axis2), norch_tensor_transpose_matmul).sum()
norch_result_transpose_matmul.backward()
norch_tensor_grad_transpose_matmul = utils.to_torch(norch_tensor_transpose_matmul.grad)
def test_transpose_axes_then_matmul(self):
"""
Test autograd from transposing a tensor with specific axes then performing matrix multiplication: matmul(tensor.transpose(axis1, axis2), tensor)
"""
norch_tensor_transpose_matmul = norch.Tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], requires_grad=True)
axis1, axis2 = 0, 1
norch_result_transpose_matmul = (norch_tensor_transpose_matmul.transpose(axis1, axis2) @ norch_tensor_transpose_matmul).sum()
norch_result_transpose_matmul.backward()
norch_tensor_grad_transpose_matmul = utils.to_torch(norch_tensor_transpose_matmul.grad)
torch_tensor_transpose_matmul = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]], dtype=torch.float32, requires_grad=True)
torch_result_transpose_matmul = torch.matmul(torch_tensor_transpose_matmul.transpose(axis1, axis2), torch_tensor_transpose_matmul).sum()
torch_result_transpose_matmul.backward()
torch_tensor_grad_transpose_matmul = torch_tensor_transpose_matmul.grad
torch_tensor_transpose_matmul = torch.tensor([[1, 2.1], [3, -4], [5, 6], [7, 8]], dtype=torch.float32, requires_grad=True)
torch_result_transpose_matmul = torch.matmul(torch_tensor_transpose_matmul.transpose(axis1, axis2), torch_tensor_transpose_matmul).sum()
torch_result_transpose_matmul.backward()
torch_tensor_grad_transpose_matmul = torch_tensor_transpose_matmul.grad
self.assertTrue(utils.compare_torch(norch_tensor_grad_transpose_matmul, torch_tensor_grad_transpose_matmul))
self.assertTrue(utils.compare_torch(norch_tensor_grad_transpose_matmul, torch_tensor_grad_transpose_matmul))
if __name__ == '__main__':
unittest.main()

29
tests/test_operations.py
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@ -2,6 +2,7 @@ import unittest
import norch
from norch import utils
import torch
import sys
class TestTensorOperations(unittest.TestCase):
def test_creation_and_conversion(self):
@ -231,6 +232,34 @@ class TestTensorOperations(unittest.TestCase):
self.assertTrue(utils.compare_torch(torch_result, torch_expected))
def test_scalar_power_tensor(self):
"""
Test scalar power of a tensor: scalar ** tensor
"""
scalar = 3
norch_tensor = norch.Tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]])
norch_result = scalar ** norch_tensor
torch_result = utils.to_torch(norch_result)
torch_tensor = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]])
torch_expected = scalar ** torch_tensor
self.assertTrue(utils.compare_torch(torch_result, torch_expected))
def test_tensor_power_scalar(self):
"""
Test tensor power of a scalar: tensor ** scalar
"""
scalar = 3
norch_tensor = norch.Tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]])
norch_result = norch_tensor ** scalar
torch_result = utils.to_torch(norch_result)
torch_tensor = torch.tensor([[[1, 2.1], [3, -4]], [[5, 6], [7, 8]]])
torch_expected = torch_tensor ** scalar
self.assertTrue(utils.compare_torch(torch_result, torch_expected))
if __name__ == '__main__':