* Make paddle no longer depend on boost

* Update enforce.h

- Added hash function inside of MKLDNN softmax op to be used as handle for primitives stroing in a
context

- Style fixes to softmax mkldnn op

- Fixes after review

- Coding style

- Fix to style

- style fixes

- style fix

- style fixes

- Fix to cody style check

- Rephrasing a comment

fix t obroken merge

Fixes to rebase

Conflicts:
	benchmark/fluid/models/machine_translation.py
	cmake/external/mkldnn.cmake
	paddle/fluid/operators/softmax_mkldnn_op.cc

- Bumped revision of MKL-DNN up to have softmax backward primitive

- Added choosing MKLDNN softmax grad operator

- First reuse of softmax backward

- Reinvented reusing for softmax

- Fix to crash in reinvented reuse

- Clang format fixes

- Clang format fixes

- Improved softmax mkldnn reuse mechanism

- clang format fixes

- Fix to broken merge

- Fix

This reverts commit 4d8e8ee2, reversing
changes made to d6a9f005.

* 1. Create buddy allocator in each places before NcclBcast the variables
2. Check the memory usage of ALL gpus rather than the first one

* 1. Make NCCLGroupGuard guards only the ncclBcast part, which avoid ncclGroupEnd blocking the exception throwing
2. NOTE the usage of NCCLGroupGuard

* Remove the memory usage check of gpus

* Fix code style

In cupti samples, only cuptiFlush is used.
I can't find any places calling cuptiFinalize and
this API can error out as not_implemented in some
cuda installation.

* Add MKLDNN layout support in Paddle

Add MKLDNN layout in Paddle so that MKLDNN friendly memory layout
can be used in MKLDNN enabled OP kernel. Before this commit, NCHW
is hardcode to be used in all MKLDNN op kernels. As a result,
non-optimized execution path is selected in MKLDNN primitive which
bring worse performance.
Besides framework change, three MKLDNN OP kernels were updated
for using new MKLDNN layout. They are conv/pool2d/batch_norm.
Other MKLDNN OP kernels need be also updated in similar way to
achieve best performance.

* Add MKLDNN layout support in activation OP

* Don't populate layout from input to output when kMKLDNN in

* Refine pool mkldnn op kernel

* MKLDNN layout

* Remove the inferitance from tensor file

* MKLDNN layout: refactoring

* Remove additional #define to register new operator

* Prepare mkldnn tests to work with layout

* Enable assertions in CUDA.

* Fix PADDLE_ASSERT.

* "fix deterministic"

* "fix ci"

* "fix init"

Sometimes dev_ctx is not available when RecordEvent.

    Experiment on vgg flower, 2 trainers, 1ps.
    more trainer could have more speedup.

    After:
    Pass = 0, Iters = 327, Speed = (7.52) img/s
    Before:
    Pass = 0, Iters = 385, Speed = (6.77) img/s