[Refactor] Refactor transfomer encode layer in ViT and BEiT backbone

[MeowZheng]

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Motivation

1. Remove redundant code
2. Modularize attention and transformer encode layer

Modification

1. Add build function for attn and ffn in TransformerEncoderLayer
2. Add attn_cfg and ffn_cfg in TransformerEncoderLayer
3. Revise qk_bias to bias in BEiTAttention

Results

Method	before refactor	inference test after refactor	training result after refactor
upernet_beit-base_8x2_640x640_160k_ade20k	53.08	53.08	52.85
upernet_vit-b16_mln_512x512_160k_ade20k		47.76	48.40

BC-breaking (Optional)

Does the modification introduce changes that break the backward-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

Use cases (Optional)

If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.

Checklist

1. Pre-commit or other linting tools are used to fix the potential lint issues.
2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D.
4. The documentation has been modified accordingly, like docstring or example tutorials.

[codecov]

Codecov Report

Merging #1481 (8c63256) into master (5b605b0) will decrease coverage by 0.00%.
The diff coverage is 94.73%.

@@            Coverage Diff             @@
##           master    #1481      +/-   ##
==========================================
- Coverage   90.30%   90.30%   -0.01%     
==========================================
  Files         140      140              
  Lines        8335     8345      +10     
  Branches     1400     1403       +3     
==========================================
+ Hits         7527     7536       +9     
- Misses        570      571       +1     
  Partials      238      238              

Flag	Coverage Δ
unittests	90.30% <94.73%> (-0.01%)	⬇️

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Impacted Files	Coverage Δ
mmseg/models/backbones/beit.py	91.83% <93.33%> (-0.51%)	⬇️
mmseg/models/backbones/vit.py	90.90% <100.00%> (+0.32%)	⬆️
mmseg/models/losses/accuracy.py	100.00% <0.00%> (ø)
mmseg/models/segmentors/base.py	59.20% <0.00%> (+1.34%)	⬆️

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