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Source code for torch.autograd.gradcheck

import torch
from torch._six import container_abcs, istuple
import torch.testing
import sys
from itertools import product
import warnings


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, container_abcs.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, dtype=input.dtype)
    elif isinstance(input, container_abcs.Iterable) and not isinstance(input, str):
        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, container_abcs.Iterable) and not isinstance(x, str):
        for elem in x:
            for result in iter_tensors(elem, only_requiring_grad):
                yield result


def get_numerical_jacobian(fn, input, target=None, eps=1e-3):
    """
    input: input to `fn`
    target: 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`.
    """
    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
        if x_tensor.is_sparse:
            def get_stride(size):
                dim = len(size)
                tmp = 1
                stride = [0] * dim
                for i in reversed(range(dim)):
                    stride[i] = tmp
                    tmp *= size[i]
                return stride

            x_nnz = x_tensor._nnz()
            x_size = list(x_tensor.size())
            x_indices = x_tensor._indices().t()
            x_values = x_tensor._values().data
            x_stride = get_stride(x_size)

            for i in range(x_nnz):
                x_value = x_values[i]
                for x_idx in product(*[range(m) for m in x_values.size()[1:]]):
                    indices = x_indices[i].tolist() + list(x_idx)
                    d_idx = sum(indices[k] * x_stride[k] for k in range(len(x_size)))

                    orig = x_value[x_idx].item()
                    x_value[x_idx] = orig - eps
                    outa = fn(input).clone()
                    x_value[x_idx] = orig + eps
                    outb = fn(input).clone()
                    x_value[x_idx] = orig
                    r = (outb - outa) / (2 * eps)
                    d_tensor[d_idx] = r.detach().reshape(-1)
        else:
            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):
    # it is easier to call to_dense() on the sparse output than
    # to modify analytical jacobian
    if output.is_sparse:
        raise ValueError('Sparse output is not supported at gradcheck yet. '
                         'Please call to_dense() on the output of fn for gradcheck.')
    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 istuple(x):
        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)


[docs]def gradcheck(func, inputs, eps=1e-6, atol=1e-5, rtol=1e-3, raise_exception=True, check_sparse_nnz=False): r"""Check gradients computed via small finite differences against analytical gradients w.r.t. tensors in :attr:`inputs` that are of floating point type and with ``requires_grad=True``. The check between numerical and analytical gradients uses :func:`~torch.allclose`. .. note:: The default values are designed for :attr:`input` of double precision. This check will likely fail if :attr:`input` is of less precision, e.g., ``FloatTensor``. .. warning:: If any checked tensor in :attr:`input` has overlapping memory, i.e., different indices pointing to the same memory address (e.g., from :func:`torch.expand`), this check will likely fail because the numerical gradients computed by point perturbation at such indices will change values at all other indices that share the same memory address. Args: func (function): a Python function that takes Tensor inputs and returns a Tensor or a tuple of Tensors inputs (tuple of Tensor or Tensor): inputs to the function eps (float, optional): perturbation for finite differences atol (float, optional): absolute tolerance rtol (float, optional): relative tolerance raise_exception (bool, optional): indicating whether to raise an exception if the check fails. The exception gives more information about the exact nature of the failure. This is helpful when debugging gradchecks. check_sparse_nnz (bool, optional): if True, gradcheck allows for SparseTensor input, and for any SparseTensor at input, gradcheck will perform check at nnz positions only. Returns: True if all differences satisfy allclose condition """ def fail_test(msg): if raise_exception: raise RuntimeError(msg) return False tupled_inputs = _as_tuple(inputs) if any(t.is_sparse for t in tupled_inputs if isinstance(t, torch.Tensor)) and not check_sparse_nnz: fail_test('gradcheck expects all tensor inputs ' 'are dense when check_sparse_nnz is set to False.') # Make sure that gradients are saved for all inputs any_input_requiring_grad = False for inp in tupled_inputs: if isinstance(inp, torch.Tensor): if inp.requires_grad: if inp.dtype != torch.float64: warnings.warn( 'At least one of the inputs that requires gradient ' 'is not of double precision floating point. ' 'This check will likely fail if all the inputs are ' 'not of double precision floating point. ') any_input_requiring_grad = True inp.retain_grad() if not any_input_requiring_grad: raise ValueError( 'gradcheck expects at least one input tensor to require gradient, ' 'but none of the them have requires_grad=True.') output = _differentiable_outputs(func(*tupled_inputs)) 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, tupled_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 torch.allclose(a, n, rtol, atol): 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(*tupled_inputs)) if any([o.requires_grad for o in output]): diff_input_list = list(iter_tensors(tupled_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 isinstance(gi, torch.Tensor) and gi.is_sparse: if gi.layout != i.layout: return fail_test('grad is sparse tensor, but has incorrect layout') if gi.sparse_dim() != i.sparse_dim(): return fail_test('grad is sparse tensor, but has incorrect sparse_dim') if gi.dense_dim() != i.dense_dim(): return fail_test('grad is sparse tensor, but has incorrect dense_dim') gi = gi.to_dense() i = i.to_dense() 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
[docs]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): r"""Check gradients of gradients computed via small finite differences against analytical gradients w.r.t. tensors in :attr:`inputs` and :attr:`grad_outputs` that are of floating point type and with ``requires_grad=True``. This function checks that backpropagating through the gradients computed to the given :attr:`grad_outputs` are correct. The check between numerical and analytical gradients uses :func:`~torch.allclose`. .. note:: The default values are designed for :attr:`input` and :attr:`grad_outputs` of double precision. This check will likely fail if they are of less precision, e.g., ``FloatTensor``. .. warning:: If any checked tensor in :attr:`input` and :attr:`grad_outputs` has overlapping memory, i.e., different indices pointing to the same memory address (e.g., from :func:`torch.expand`), this check will likely fail because the numerical gradients computed by point perturbation at such indices will change values at all other indices that share the same memory address. Args: func (function): a Python function that takes Tensor inputs and returns a Tensor or a tuple of Tensors inputs (tuple of Tensor or Tensor): inputs to the function grad_outputs (tuple of Tensor or 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, optional): indicating whether to raise an exception if the check 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) 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(*tupled_inputs)) tupled_grad_outputs = tuple(randn_like(x) for x in outputs) else: tupled_grad_outputs = _as_tuple(grad_outputs) num_outputs = len(tupled_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, tupled_inputs + tupled_grad_outputs, eps, atol, rtol, raise_exception)

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