docs fixes for mobile docs

Author: prigoyal

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;
+    }
+}

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.

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

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
@@ -36,7 +36,7 @@ to TensorDot operation.
 
 A simple 2D matrix multiply operation in TC is expressed as:
 
-.. code-block:: python
+.. code::
 
      def matmul(float(M, N) X, float(N, K) W) -> (output) {
          output(m, k) +=! X(m, nn) * W(nn, k)
@@ -47,7 +47,7 @@ The variable :code:`nn` is being reduced in above expression. Now, let's write a
 **batched matrix-multiply** operation using above expression. For that, we need to
 add a batch dimension to it and the expression becomes:
 
-.. code-block:: python
+.. code::
 
      def batch_matmul(float(B, M, N) X, float(B, N, K) W) -> (output) {
          output(b, m, k) +=! X(b, m, nn) * W(b, nn, k)
@@ -56,7 +56,7 @@ add a batch dimension to it and the expression becomes:
 Now, for the tensordot operation, we need to add spatial dimensions :code:`H` and :code:`W`
 to the batched matrix multiply, and the expression for TensorDot becomes:
 
-.. code-block:: python
+.. code::
 
      def tensordot(float(B, C1, C2, H, W) I0, float(B, C2, C3, H, W) I1) -> (O) {
          O(b, c1, c3, h, w) +=! I0(b, c1, c2, h, w) * I1(b, c2, c3, h, w)
@@ -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