def test_mul():
"""Tests that mytorch's elementwise multiplication matches torch's"""
# shape of tensor to test
shape = (1, 2, 3)
# get mytorch and torch tensor: 'a'
a = Tensor.randn(*shape)
a.requires_grad = True
a_torch = get_same_torch_tensor(a)
# get mytorch and torch tensor: 'b'
b = Tensor.randn(*shape)
b.requires_grad = True
b_torch = get_same_torch_tensor(b)
# run mytorch and torch forward: 'c = a * b'
ctx = ContextManager()
c = Mul.forward(ctx, a, b)
c_torch = a_torch * b_torch
# run mytorch and torch multiplication backward
back = Mul.backward(ctx, Tensor.ones(*shape))
c_torch.sum().backward()
# check that c matches
assert check_val_and_grad(c, c_torch)
# check that dc/da and dc/db respectively match
assert check_val(back[0], a_torch.grad)
assert check_val(back[1], b_torch.grad)
# ensure * is overridden
c_using_override = a * b
assert check_val(c_using_override, c_torch)
return True
def test_matmul():
"""Tests that mytorch's matmul multiplication matches torch's"""
# shape of tensor to test
shape1 = (3, 4)
shape2 = (4, 5)
shape = (3, 5)
# get mytorch and torch tensor: 'a'
a = Tensor.randn(*shape1)
a.requires_grad = True
a_torch = get_same_torch_tensor(a)
# get mytorch and torch tensor: 'b'
b = Tensor.randn(*shape2)
b.requires_grad = True
b_torch = get_same_torch_tensor(b)
# run mytorch and torch forward: 'c = a * b'
ctx = ContextManager()
c = matmul.forward(ctx, a, b)
c_torch = torch.matmul(a_torch, b_torch)
# run mytorch and torch multiplication backward
back = matmul.backward(ctx, Tensor.ones(*shape))
c_torch.sum().backward()
# check that c matches
assert check_val_and_grad(c, c_torch)
# check that dc/da and dc/db respectively match
assert check_val(back[0], a_torch.grad)
assert check_val(back[1], b_torch.grad)
return True
def test_sub():
"""Tests that mytorch subtraction matches torch's subtraction"""
# shape of tensor to test
shape = (1, 2, 3)
# get mytorch and torch tensor: 'a'
a = Tensor.randn(*shape)
a.requires_grad = True
a_torch = get_same_torch_tensor(a)
# get mytorch and torch tensor: 'b'
b = Tensor.randn(*shape)
b.requires_grad = True
b_torch = get_same_torch_tensor(b)
# run mytorch and torch forward: 'c = a - b'
ctx = ContextManager()
c = Sub.forward(ctx, a, b)
c_torch = a_torch - b_torch
# run mytorch and torch subtraction backward
back = Sub.backward(ctx, Tensor.ones(*shape))
c_torch.sum().backward()
# check that c matches
assert check_val_and_grad(c, c_torch)
# check that dc/da and dc/db respectively match
assert check_val(back[0], a_torch.grad)
# print(f'Sub back[1] == {back[1]} and back[0] == {back[0]} and b_torch.grad == {b_torch.grad}')
assert check_val(back[1], b_torch.grad)
# ensure - is overridden
c_using_override = a - b
assert check_val(c_using_override, c_torch)
return True
