Cambricon / mlu-ops

Efficient operation implementation based on the Cambricon Machine Learning Unit (MLU) .
MIT License
103 stars 102 forks source link

[Feature](mlu-ops): delete 200 code and adapt to toolkit 4.0 #1105

Closed DanieeelLiu closed 2 weeks ago

DanieeelLiu commented 2 weeks ago

Thanks for your contribution and we appreciate it a lot. :rocket::rocket:

1. Motivation

Please describe your motivation and the goal you want to achieve through this pull request.

2. Modification

Please briefly describe what modification is made in this pull request, and indicate where to make the modification.

Are new test cases added? If so, please post the corresponding generator-PR link here.

3. Test Report

If you want to know how to do operator testing, you can see GTest-User-Guide-zh.

3.1 Modification Details

3.1.1 Accuracy Acceptance Standard

For static threshold standard details, see: MLU-OPS™ Accuracy Acceptance Standard.

3.1.2 Operator Scheme checklist

3.2 Accuracy Test

3.2.1 Accuracy Test

If you have checked the following items, please tick the relevant box.

3.2.2 Parameter Check

Test Point-1: When a new operator is submitted, the test points are given and the test results are stated. Acceptance Standard: Normal error.

Please fill your test results(Error Message) in here, ...

Test Point-2: Whether illegal parameters are passed. Acceptance Standard: Normal error.

Test results...

3.3 Performance Test

See MLU-OPS™ Performance Acceptance Standard for details.

Platform:MLU370

# The test results should contain Op name, Shape, Data type,  
#   MLU Hardware Time(us), MLU Interface Time(us), MLU IO Efficiency, 
#   MLU Compute Efficiency, and Mlu Workspace Size(Bytes)
# 
# for example:
#
# ----------- case0 -----------
# case0
# [Op name                ]: abs
# [Shape                  ]: input.shape=[1024,1024,3,4], output.shape=[1024,1024,3,4]
# [Data type]             ]: float32
# [MLU Hardware Time      ]: 15728 (us)
# [MLU Interface Time     ]: 369.008 (us)
# [MLU IO Efficiency      ]: 0.23275
# [MLU Compute Efficiency ]: 0.5
# [Mlu Workspace Size     ]: -1 (Bytes)
# 
# ----------- case1 -----------
# ...

Platform:MLU590

# ----------- case0 -----------
# ----------- case1 -----------
# ...

3.4 Summary Analysis

Please give a brief overview here, if you want to note and summarize the content. precheckin 实际已通过