| import torch |
| from collections import Iterable |
| import torch.testing |
| import sys |
| from itertools import product |
| |
| |
| def zero_gradients(x): |
| if isinstance(x, torch.Tensor): |
| if x.grad is not None: |
| x.grad.detach_() |
| x.grad.data.zero_() |
| elif isinstance(x, Iterable): |
| for elem in x: |
| zero_gradients(elem) |
| |
| |
| def make_jacobian(input, num_out): |
| if isinstance(input, torch.Tensor): |
| if not input.is_floating_point(): |
| return None |
| if not input.requires_grad: |
| return None |
| return torch.zeros(input.nelement(), num_out) |
| elif isinstance(input, Iterable): |
| jacobians = list(filter( |
| lambda x: x is not None, (make_jacobian(elem, num_out) for elem in input))) |
| if not jacobians: |
| return None |
| return type(input)(jacobians) |
| else: |
| return None |
| |
| |
| def iter_tensors(x, only_requiring_grad=False): |
| if isinstance(x, torch.Tensor): |
| if x.requires_grad or not only_requiring_grad: |
| yield x |
| elif isinstance(x, Iterable): |
| for elem in x: |
| for result in iter_tensors(elem, only_requiring_grad): |
| yield result |
| |
| |
| # `input` is input to `fn` |
| # `target` is the Tensors wrt whom Jacobians are calculated (default=`input`) |
| # |
| # Note that `target` may not even be part of `input` to `fn`, so please be |
| # **very careful** in this to not clone `target`. |
| def get_numerical_jacobian(fn, input, target=None, eps=1e-3): |
| if target is None: |
| target = input |
| output_size = fn(input).numel() |
| jacobian = make_jacobian(target, output_size) |
| |
| # It's much easier to iterate over flattened lists of tensors. |
| # These are reference to the same objects in jacobian, so any changes |
| # will be reflected in it as well. |
| x_tensors = [t for t in iter_tensors(target, True)] |
| j_tensors = [t for t in iter_tensors(jacobian)] |
| |
| # TODO: compare structure |
| for x_tensor, d_tensor in zip(x_tensors, j_tensors): |
| # need data here to get around the version check because without .data, |
| # the following code updates version but doesn't change content |
| x_tensor = x_tensor.data |
| for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])): |
| orig = x_tensor[x_idx].item() |
| x_tensor[x_idx] = orig - eps |
| outa = fn(input).clone() |
| x_tensor[x_idx] = orig + eps |
| outb = fn(input).clone() |
| x_tensor[x_idx] = orig |
| |
| r = (outb - outa) / (2 * eps) |
| d_tensor[d_idx] = r.detach().reshape(-1) |
| |
| return jacobian |
| |
| |
| def get_analytical_jacobian(input, output): |
| diff_input_list = list(iter_tensors(input, True)) |
| jacobian = make_jacobian(input, output.numel()) |
| jacobian_reentrant = make_jacobian(input, output.numel()) |
| grad_output = torch.zeros_like(output) |
| flat_grad_output = grad_output.view(-1) |
| reentrant = True |
| correct_grad_sizes = True |
| |
| for i in range(flat_grad_output.numel()): |
| flat_grad_output.zero_() |
| flat_grad_output[i] = 1 |
| for jacobian_c in (jacobian, jacobian_reentrant): |
| grads_input = torch.autograd.grad(output, diff_input_list, grad_output, |
| retain_graph=True, allow_unused=True) |
| for jacobian_x, d_x, x in zip(jacobian_c, grads_input, diff_input_list): |
| if d_x is not None and d_x.size() != x.size(): |
| correct_grad_sizes = False |
| elif jacobian_x.numel() != 0: |
| if d_x is None: |
| jacobian_x[:, i].zero_() |
| else: |
| d_x_dense = d_x.to_dense() if d_x.is_sparse else d_x |
| assert jacobian_x[:, i].numel() == d_x_dense.numel() |
| jacobian_x[:, i] = d_x_dense.contiguous().view(-1) |
| |
| for jacobian_x, jacobian_reentrant_x in zip(jacobian, jacobian_reentrant): |
| if jacobian_x.numel() != 0 and (jacobian_x - jacobian_reentrant_x).abs().max() != 0: |
| reentrant = False |
| |
| return jacobian, reentrant, correct_grad_sizes |
| |
| |
| def _as_tuple(x): |
| if isinstance(x, tuple): |
| return x |
| elif isinstance(x, list): |
| return tuple(x) |
| else: |
| return x, |
| |
| |
| def _differentiable_outputs(x): |
| return tuple(o for o in _as_tuple(x) if o.requires_grad) |
| |
| |
| def gradcheck(func, inputs, eps=1e-6, atol=1e-5, rtol=1e-3, raise_exception=True): |
| """Check gradients computed via small finite differences |
| against analytical gradients |
| |
| The check between numerical and analytical has the same behaviour as |
| numpy.allclose https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html |
| meaning it check that |
| absolute(a - n) <= (atol + rtol * absolute(n)) |
| is true for all elements of analytical jacobian a and numerical jacobian n. |
| |
| Args: |
| func: Python function that takes Tensor inputs and returns |
| a Tensor or a tuple of Tensors |
| inputs: tuple of Tensors |
| eps: perturbation for finite differences |
| atol: absolute tolerance |
| rtol: relative tolerance |
| raise_exception: bool indicating whether to raise an exception if |
| gradcheck fails. The exception gives more information about the |
| exact nature of the failure. This is helpful when debugging gradchecks. |
| Returns: |
| True if all differences satisfy allclose condition |
| """ |
| tupled_inputs = _as_tuple(inputs) |
| |
| # Make sure that gradients are saved for all inputs |
| for inp in tupled_inputs: |
| if isinstance(inp, torch.Tensor): |
| inp.retain_grad() |
| |
| output = _differentiable_outputs(func(*inputs)) |
| |
| def fail_test(msg): |
| if raise_exception: |
| raise RuntimeError(msg) |
| return False |
| |
| for i, o in enumerate(output): |
| if not o.requires_grad: |
| continue |
| |
| def fn(input): |
| return _as_tuple(func(*input))[i] |
| |
| analytical, reentrant, correct_grad_sizes = get_analytical_jacobian(tupled_inputs, o) |
| numerical = get_numerical_jacobian(fn, inputs, eps=eps) |
| |
| if not correct_grad_sizes: |
| return fail_test('Analytical gradient has incorrect size') |
| |
| for j, (a, n) in enumerate(zip(analytical, numerical)): |
| if a.numel() != 0 or n.numel() != 0: |
| if not ((a - n).abs() <= (atol + rtol * n.abs())).all(): |
| return fail_test('Jacobian mismatch for output %d with respect to input %d,\n' |
| 'numerical:%s\nanalytical:%s\n' % (i, j, n, a)) |
| |
| if not reentrant: |
| return fail_test('Backward is not reentrant, i.e., running backward with same ' |
| 'input and grad_output multiple times gives different values, ' |
| 'although analytical gradient matches numerical gradient') |
| |
| # check if the backward multiplies by grad_output |
| output = _differentiable_outputs(func(*inputs)) |
| if any([o.requires_grad for o in output]): |
| diff_input_list = list(iter_tensors(inputs, True)) |
| if not diff_input_list: |
| raise RuntimeError("no Tensors requiring grad found in input") |
| grads_input = torch.autograd.grad(output, diff_input_list, [torch.zeros_like(o) for o in output], |
| allow_unused=True) |
| for gi, i in zip(grads_input, diff_input_list): |
| if gi is None: |
| continue |
| if not gi.eq(0).all(): |
| return fail_test('backward not multiplied by grad_output') |
| if gi.type() != i.type(): |
| return fail_test("grad is incorrect type") |
| if gi.size() != i.size(): |
| return fail_test('grad is incorrect size') |
| |
| return True |
| |
| |
| def gradgradcheck(func, inputs, grad_outputs=None, eps=1e-6, atol=1e-5, rtol=1e-3, |
| gen_non_contig_grad_outputs=False, raise_exception=True): |
| """Check gradients of gradients computed via small finite differences |
| against analytical gradients |
| This function checks that backpropagating through the gradients computed |
| to the given grad_outputs are correct. |
| |
| The check between numerical and analytical has the same behaviour as |
| numpy.allclose https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html |
| meaning it check that |
| absolute(a - n) <= (atol + rtol * absolute(n)) |
| is true for all elements of analytical gradient a and numerical gradient n. |
| |
| Args: |
| func (function): Python function that takes Tensor inputs and returns |
| a Tensor or a tuple of Tensors |
| inputs (tuple of Tensor): inputs to the function |
| grad_outputs (tuple of Tensor, optional): The gradients with respect to |
| the function's outputs. |
| eps (float, optional): perturbation for finite differences |
| atol (float, optional): absolute tolerance |
| rtol (float, optional): relative tolerance |
| gen_non_contig_grad_outputs (bool, optional): if :attr:`grad_outputs` is |
| ``None`` and :attr:`gen_non_contig_grad_outputs` is ``True``, the |
| randomly generated gradient outputs are made to be noncontiguous |
| raise_exception: bool indicating whether to raise an exception if |
| gradcheck fails. The exception gives more information about the |
| exact nature of the failure. This is helpful when debugging gradchecks. |
| |
| Returns: |
| True if all differences satisfy allclose condition. Raises an exception |
| otherwise. |
| """ |
| if grad_outputs is None: |
| # If grad_outputs is not specified, create random Tensors of the same |
| # shape, type, and device as the outputs |
| def randn_like(x): |
| y = torch.testing.randn_like(x if x.is_floating_point() else x.double()) |
| if gen_non_contig_grad_outputs: |
| y = torch.testing.make_non_contiguous(y) |
| return y.requires_grad_() |
| outputs = _as_tuple(func(*inputs)) |
| grad_outputs_gen = (randn_like(x) for x in outputs) |
| grad_outputs = list(grad_outputs_gen) if not isinstance(inputs, tuple) else tuple(grad_outputs_gen) |
| |
| num_outputs = len(grad_outputs) |
| |
| def new_func(*args): |
| input_args = args[:-num_outputs] |
| grad_outputs = args[-num_outputs:] |
| outputs = _differentiable_outputs(func(*input_args)) |
| input_args = tuple(x for x in input_args if isinstance(x, torch.Tensor) and x.requires_grad) |
| grad_inputs = torch.autograd.grad(outputs, input_args, grad_outputs, create_graph=True) |
| return grad_inputs |
| |
| return gradcheck(new_func, inputs + grad_outputs, eps, atol, rtol, raise_exception) |
