[MXNET-1404] Implement storage tagging, the first half of the memory profiler

[ArmageddonKnight]

Description

implement storage tagging, the first half of the memory profiler

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant JIRA issue created (except PRs with tiny changes)
• Changes are complete (i.e. I finished coding on this PR)
• Code is well-documented:
• For user-facing API changes, API doc string has been updated.
• For new C++ functions in header files, their functionalities and arguments are documented.
• For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
• Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
• To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Changes

• This PR is the first half of the GPU memory profiler. It implements storage tagging which adds profiler scope, name, and data structure information to each allocated storage handle.

Example

    profiler.set_state('run')

    model = nn.HybridSequential(prefix='net_')
    with model.name_scope():
        model.add(nn.Dense(128, activation='tanh'))
        model.add(nn.Dropout(0.5))
        model.add(nn.Dense(64, activation='tanh'),
                  nn.Dense(32, in_units=64))
        model.add(nn.Activation('relu'))
    model.initialize(ctx=mx.gpu())
    model.hybridize()

    inputs = mx.sym.var('data')

    with mx.autograd.record():
        out = model(mx.nd.zeros((16, 10), ctx=mx.gpu()))
    out.backward()
    mx.nd.waitall()
    profiler.set_state('stop')
    profiler.dump(True)

The code snippet above will generate the following gpu_memory_profile.csv:

Attribute Name	Requested Size	Device	Actual Size	Reuse?
<unk>:in_arg:data	640	0	4096	0
net:arg_grad:net_dense0_bias	512	0	4096	0
net:arg_grad:net_dense0_weight	5120	0	8192	0
net:arg_grad:net_dense1_bias	256	0	4096	0
net:arg_grad:net_dense1_weight	32768	0	32768	0
net:arg_grad:net_dense2_bias	128	0	4096	0
net:arg_grad:net_dense2_weight	8192	0	8192	0
net:dense0:net_dense0_fwd	8192	0	8192	0
net:dense0:tanh:net_dense0_tanh_fwd	8192	0	8192	0
net:dense1:net_dense1_fwd	4096	0	4096	0
net:dense1:tanh:net_dense1_tanh_fwd	4096	0	4096	0
net:dense2:net_dense2_fwd	2048	0	4096	0
net:dense2:net_dense2_fwd_backward	4096	0	4096	0
net:dropout0:net_dropout0_fwd	8192	0	8192	0
net:dropout0:net_dropout0_fwd	8192	0	8192	0
net:in_arg:net_dense0_bias	512	0	4096	0
net:in_arg:net_dense0_weight	5120	0	8192	0
net:in_arg:net_dense1_bias	256	0	4096	0
net:in_arg:net_dense1_weight	32768	0	32768	0
net:in_arg:net_dense2_bias	128	0	4096	0
net:in_arg:net_dense2_weight	8192	0	8192	0
net:relu0:net_relu0_fwd	2048	0	4096	0
net:relu0:net_relu0_fwd_backward	8192	0	8192	0
net:relu0:net_relu0_fwd_head_grad	2048	0	4096	0
resource:cudnn_dropout_state (dropout-inl.h +258)	1671168	0	1671168	0
resource:temp_space (fully_connected-inl.h +316)	34816	0	36864	0