Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix building doc #912

Merged
merged 2 commits into from
Jan 29, 2022
Merged

Fix building doc #912

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9b2999b
Fix building doc
pkufool Jan 29, 2022
368f14e
Fix flake8
pkufool Jan 29, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 7 additions & 7 deletions docs/requirements.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,8 +1,8 @@
dataclasses
graphviz
recommonmark
sphinx
sphinx-autodoc-typehints
sphinx_rtd_theme
sphinxcontrib-bibtex
dataclasses==0.6
graphviz==0.19.1
recommonmark==0.7.1
sphinx==4.3.2
sphinx-autodoc-typehints==1.12.0
sphinx_rtd_theme==1.0.0
sphinxcontrib-bibtex==2.4.1
torch>=1.6.0
13 changes: 7 additions & 6 deletions k2/python/k2/mutual_information.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -143,8 +143,9 @@ def mutual_information_recursion(
return_grad:
Whether to return grads of ``px`` and ``py``, this grad standing for the
occupation probability is the output of the backward with a
``fake gradient`` input (all ones) This is useful to implement the
pruned version of rnnt loss.
``fake gradient`` the ``fake gradient`` is the same as the gradient
you'd get if you did ``torch.autograd.grad((scores.sum()), [px, py])``.
This is useful to implement the pruned version of rnnt loss.

Returns:
Returns a torch.Tensor of shape ``[B]``, containing the log of the mutual
Expand All @@ -160,8 +161,8 @@ def mutual_information_recursion(

where we handle edge cases by treating quantities with negative indexes
as **-infinity**. The extension to cases where the boundaries are
specified should be obvious; it just works on shorter sequences with offsets
into ``px`` and ``py``.
specified should be obvious; it just works on shorter sequences with
offsets into ``px`` and ``py``.
"""
assert px.ndim == 3
B, S, T1 = px.shape
Expand All @@ -179,10 +180,10 @@ def mutual_information_recursion(
assert px.is_contiguous()
assert py.is_contiguous()

m, px_grad, py_grad = MutualInformationRecursionFunction.apply(
scores, px_grad, py_grad = MutualInformationRecursionFunction.apply(
px, py, boundary, return_grad
)
return (m, (px_grad, py_grad)) if return_grad else m
return (scores, (px_grad, py_grad)) if return_grad else scores


def _inner_product(a: Tensor, b: Tensor) -> Tensor:
Expand Down