Add finer control over the pruning logic, to accommodate more pruning
use-cases.
The full description of the new logic is available in the updated [documentation
of the CompressionScheduler](https://nervanasystems.github.io/distiller/schedule.html#pruning-fine-control), which is also part of this PR.

In this PR:

* Added a new callback to the CompressionScheduler:
compression_scheduler.before_parameter_optimization which is invoked
after the gradients are are computed, but before the weights are updated
by the optimizer.

* We provide an option to mask the gradients, before the weights are updated by the optimizer. 
We register to the parameter backward hook in order to mask the gradients.
This gives us finer control over the parameter updates.

* Added several DropFilter schedules.
DropFilter is a method to regularize networks, and it can also be
used to "prepare" a network for permanent filter pruning.

*Added documentation of pruning fine-control

* Bias handling:
  * Add 'bits_bias' parameter to explicitly specify # of bits for bias,
    similar to weights and activations.
  * BREAKING: Remove the now redundant 'quantize_bias' boolean parameter
* Custom overrides:
  * Expand the semantics of the overrides dict to allow overriding of
    other parameters in addition to bit-widths
  * Functions registered in the quantizer's 'replacement_factory' can
    define keyword arguments. Non bit-width entries in the overrides
    dict will be checked against the function signature and passed
  * BREAKING:
    * Changed the name of 'bits_overrides' to simply 'overrides'
    * Bit-width overrides must now be defined using the full parameter
      names - 'bits_activations/weights/bias' instead of the short-hands
      'acts' and 'wts' which were used so far.
  * Added/updated relevant tests
  * Modified all quantization YAMLs under 'examples' to reflect 
    these changes
  * Updated docs

Not backward compatible - re-installation is required

* Fixes for PyTorch==1.0.0
* Refactoring folder structure
* Update installation section in docs

Summary of changes:
(1) Post-train quantization based on pre-collected statistics
(2) Quantized concat, element-wise addition / multiplication and embeddings
(3) Move post-train quantization command line args out of sample code
(4) Configure post-train quantization from YAML for more fine-grained control

(See PR #136 for more detailed changes descriptions)

- Moved the Language model and struct pruning tutorials from the Wiki to
the HTML documentation.  Love the ease of Wiki, but GitHub doesn't let
Google crawl these pages, and users can't open PRs on Wiki pages.

- Updated the pruning algorithms documentation

* Asymmetric post-training quantization (only symmetric supported so until now)
* Quantization aware training for range-based (min-max) symmetric and asymmetric quantization
* Per-channel quantization support in both training and post-training
* Added tests and examples
* Updated documentation

Thanks to Dan Alistarh for bringing this issue to my attention.
The activations of Linear layers have shape (batch_size, output_size) and those
of Convolution layers have shape (batch_size, num_channels, width, height) and
this distinction in shape was not correctly handled.

This commit also fixes sparsity computation for very large activations, as seen
in VGG16, which leads to memory exhaustion.  One solution is to use smaller
batch sizes, but this commit uses a different solution, which counts zeros “manually”,
and using less space.

Also in this commit:
- Added a “caveats” section to the documentation.
- Added more tests.

Add docs/conditional_computation.md which was accidentally left out of an
earlier commit.

* Updated stats computation - fixes issues with validation stats

* Clarification of output (docs)

* Update

* Moved validation stats to separate function

* Fixed validation stats and added new summary stats

* Trimmed some comments.

* Improved figure for documentation

* Minor updates

Latest versions of Jupyter notebooks have a different syntax for
launching the server such that it listens on oll network interfaces
(this is useful if you are running the Jupyter server on one machine,
and connect to it from a browser on a different machine).

So:
	jupyter-notebook --ip=* --no-browser

is replaced by:
	jupyter-notebook --ip=0.0.0.0 --no-browser

* A temporary fix for issue 36

The thinning code assumes that the sgraph it is using
is not data-parallel, because it (currently) accesses the
layer-name keys using a "normalized" name ("module." is removed).

The bug is that in thinning.py#L73 we create a data_parallel=True
model; and then give it to sgraph.
But in other places thinning code uses "normalized" keys.  For
example in thinning.py#L264.

The temporary fix configures data_parallel=False in thinning.py#L73.

A long term solution should have SummaryGraph know how to handle
both parallel and not-parallel models.  This can be done by having
SummaryGraph convert layer-names it receives in the API to
data_parallel=False using normalize_layer_name.  When returning
results, use the de-normalized format.

* Fix the documentation error from issue 36
* Move some logs to debug and show in logging.conf how to enable DEBUG logs.

* Implemented as a Policy
* Integrated in image classification sample
* Updated docs and README

Enabling Early Exit strategy in image classifier example

* Adding PACT quantization method
* Move logic modifying the optimizer due to changes the quantizer makes into the Quantizer itself
* Updated documentation and tests

* Add Quantizer unit tests
* Require 'bits_overrides' to be OrderedDict to support overlapping
  patterns in a predictable manner + update documentation to reflect this
* Quantizer class cleanup
  * Use "public" nn.Module APIs instead of protected attributes
  * Call the builtins set/get/delattr instead of the class special methods
    (__***__)
  * Fix issues reported in #24
* Bug in RangeLinearQuantParamLayerWrapper - add explicit override of
  pre_quantized_forward accpeting single input (#15)
* Add DoReFa test to full_flow_tests

* Scale of bias and parentheses were wrong

Reviewed and looking good.  We have to set a convention for naming files.

Two places in the documentation gave the wrong path to the example
Alexnet sensitivity pruning schedule.