Merge pull request #87 from facebookresearch/mobile-css-theme

nits and docs fixes for mobile docs
facebookresearch · Feb 28, 2018 · f0c47fe · f0c47fe
2 parents 045132d + ae10ffb
commit f0c47fe
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Showing 4 changed files with 26 additions and 5 deletions.
diff --git a/docs/source/_static/css/tc_theme.css b/docs/source/_static/css/tc_theme.css
@@ -90,3 +90,24 @@ footer .rst-footer-buttons {
 footer p {
     font-size: 100%;
 }
+
+/* Fixes for mobile - adopted from pytorch theme*/
+.wy-nav-top {
+    background-color: #fff;
+    background-image: url('../img/tc-logo-full-color-with-text-2.png');
+    background-repeat: no-repeat;
+    background-position: center;
+    padding: 0;
+    margin: 0.4045em 0.809em;
+    color: #333;
+}
+
+.wy-nav-top > a {
+    display: none;
+}
+
+@media screen and (max-width: 768px) {
+    .wy-side-nav-search>a img.logo {
+        height: 60px;
+    }
+}
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -71,9 +71,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = 'v0.1.0'
+version = 'v0.1.1'
 # The full version, including alpha/beta/rc tags.
-release = 'v0.1.0'
+release = 'v0.1.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

diff --git a/docs/source/framework/pytorch_integration/layers_database.rst b/docs/source/framework/pytorch_integration/layers_database.rst
@@ -256,7 +256,7 @@ Cast
 .. code::
 
     def cast(float(M,N) A) -> (int32(M,N) O1) {{
-        O1(m, n) = int32(A(m, n) + {four})
+        O1(m, n) = int32(A(m, n) + {constant})
     }}
 
 Copy

diff --git a/docs/source/tutorials/tutorial_tensordot_with_tc.rst b/docs/source/tutorials/tutorial_tensordot_with_tc.rst
@@ -10,7 +10,7 @@ For this tutorial, you will need to install Tensor Comprehensions binary. You ca
 get binary builds of Tensor Comprehensions with: ``conda install -y -c pytorch -c prigoyal tensor_comprehensions``
 
 About TensorDot
-^^^^^^^^^^^^^^^
+---------------
 
 Assume that we have two tensors, one with dimension :code:`(N, C1, C2, H, W)` and
 one with dimension :code:`(N, C2, C3, H, W)`, and we want to do a gemm-type
@@ -140,7 +140,7 @@ get a decent kernel performance as shown in the screenshot below (tuned on one M
     :align: center
 
 Early stopping
-^^^^^^^^^^^^^^
+--------------
 
 If your kernel performance is good enough while the autotuning continues, you
 can stop autotuning by pressing :code:`Ctrl+C` and the autotuning cache will be saved