Restore fast convolution functions and rewrites, as well as convolution docs

Restore fast convolution functions and rewrites, as well as convolution docs

Author: jessegrabowski

doc/library/tensor/conv.rst

@@ -1,11 +1,68 @@
-=========================================
-:mod:`tensor.conv` -- Tensor Convolutions
-=========================================
+.. _libdoc_tensor_conv:
 
-.. module:: tensor.conv
+==========================================================
+:mod:`conv` -- Ops for convolutional neural nets
+==========================================================
+
+.. module:: conv
    :platform: Unix, Windows
-   :synopsis: Tensor Convolutions
-.. moduleauthor:: LISA, PyMC Developers, PyTensor Developers
+   :synopsis: ops for signal processing
+.. moduleauthor:: LISA
+
+
+The recommended user interface are:
+
+- :func:`aesara.tensor.conv.conv2d` for 2d convolution
+- :func:`aesara.tensor.conv.conv3d` for 3d convolution
+
+Pytensor will automatically use the fastest implementation in many cases.
+On the CPU, the implementation is a GEMM based one.
+
+This auto-tuning has the inconvenience that the first call is much
+slower as it tries and times each implementation it has. So if you
+benchmark, it is important that you remove the first call from your
+timing.
+
+Implementation Details
+======================
+
+This section gives more implementation detail. Most of the time you do
+not need to read it. Pytensor will select it for you.
+
+
+- Implemented operators for neural network 2D / image convolution:
+    - :func:`conv.conv2d <pytensor.tensor.conv.conv2d>`.
+      old 2d convolution. DO NOT USE ANYMORE.
+
+      For each element in a batch, it first creates a
+      `Toeplitz <http://en.wikipedia.org/wiki/Toeplitz_matrix>`_ matrix in a CUDA kernel.
+      Then, it performs a ``gemm`` call to multiply this Toeplitz matrix and the filters
+      (hence the name: MM is for matrix multiplication).
+      It needs extra memory for the Toeplitz matrix, which is a 2D matrix of shape
+      ``(no of channels * filter width * filter height, output width * output height)``.
+
+    - :func:`CorrMM <pytensor.tensor.nnet.corr.CorrMM>`
+      This is a CPU-only 2d correlation implementation taken from
+      `caffe's cpp implementation <https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cpp>`_.
+      It does not flip the kernel.
+
+- Implemented operators for neural network 3D / video convolution:
+    - :func:`Corr3dMM <pytensor.tensor.nnet.corr3d.Corr3dMM>`
+      This is a CPU-only 3d correlation implementation based on
+      the 2d version (:func:`CorrMM <pytensor.tensor.nnet.corr.CorrMM>`).
+      It does not flip the kernel. As it provides a gradient, you can use it as a
+      replacement for nnet.conv3d. For convolutions done on CPU,
+      nnet.conv3d will be replaced by Corr3dMM.
+
+    - :func:`conv3d2d <pytensor.tensor.nnet.conv3d2d.conv3d>`
+      Another conv3d implementation that uses the conv2d with data reshaping.
+      It is faster in some corner cases than conv3d. It flips the kernel.
+
+.. autofunction:: pytensor.tensor.conv.conv2d
+.. autofunction:: pytensor.tensor.conv.conv2d_transpose
+.. autofunction:: pytensor.tensor.conv.conv3d
+.. autofunction:: pytensor.tensor.conv.conv3d2d.conv3d
+.. autofunction:: pytensor.tensor.conv.conv.conv2d
 
-.. automodule:: pytensor.tensor.conv
-    :members:
+.. automodule:: pytensor.tensor.conv.abstract_conv
+    :members: