(we use 8-bit values below, but this applies to any bit-width)
* We use the notion of "full" and "restricted" quantized range for
  symmetric quantization (see section 2.2 in https://arxiv.org/abs/1806.08342)
* "Full" quantized range ==> [-128, 127], "restircted" ==> [-127, 127]
* Until now, when doing symmetric quantization we assumed a "full"
  range when saturating after quantization, but calculated the scale
  factor as if the range was restricted. This means we weren't making
  full utilization of the quantized range.
* On the other hand, in some other implementations of quantization (e.g.
  TensorFlow), the "restricted" range is used.
* So, we make it an option to use either the proper "full" range
  (q_min = -128) or "restricted" range (q_min = -127).
* LinearQuantMode.SYMMETRIC now means the "full" range is used, and
  added LinearQuantMode.SYMMETRIC_RESTRICTED for using the "restricted"
  range.
* Updated tests and documentation.

ACIQ clipping method, as described in:

Post training 4-bit quantization of convolutional networks for rapid-deployment
(Ron Banner , Yury Nahshan, Daniel Soudry)
(NeurIPS 2019)

https://arxiv.org/abs/1810.05723



Co-authored-by: Yury Nahshan <yury.nahshan@intel.com>
Co-authored-by: Lev Zlotnik <lev.zlotnik@intel.com>

* PyTorch 1.1.0 now required
  - Moved other dependencies to up-to-date versions as well
* Adapt LR scheduler to PyTorch 1.1 API changes:
  - Change lr_scheduler.step() calls to succeed validate calls,
    during training
  - Pass to lr_scheduler.step() caller both loss and top1
    (Resolves issue #240)
* Adapt thinning for PyTorch 1.1 semantic changes
  - **KNOWN ISSUE**: When a thinning recipe is applied, in certain
    cases PyTorch displays this warning:
    "UserWarning: non-inplace resize is deprecated".
    To be fixed later
* SummaryGraph: Workaround for new scope name issue from PyTorch 1.1.0
* Adapt to updated PyTest version:
  - Stop using deprecated 'message' parameter of pytest.raises(),
    use pytest.fail() instead
  - Make sure only a single test case per pytest.raises context
* Move PyTorch version check to root __init__.py 
  - This means the version each checked when Distiller is first
    imported. A RuntimeError is raised if the version is wrong.
* Updates to parameter_histograms notebook:
  - Replace deprecated normed argument with density
  - Add sparsity rate to plot title
  - Load model in CPU

* Some refactoring to enable multiple clipping methods
* BREAKING: clip_acts as a boolean flag (either in command line
  or in function signature) will fail. Error message with valid
  values from is displayed.
* Implemented clipping activations at mean + N * std
  (N is user configurable)
* Additional tests
* Updated docs

* Always include 0 in the range
* Handle case where tensor is zeros only (fixes issue #115)
* Add unit tests