We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
V0.3.0 Release Plan
# Release Manager
@TobeyQin 

# Endgame
Code freeze: 9/1/2021
Bug Bash date: 9/2/2021
Release date: 9/17/2021

# Main Features
## SuperBench Framework

### SB Runner -- @abuccts 

- [x] MPI mode implementation
       PR: #146

### SB Benchmarks -- @guoshzhao 

- [x] Docker Base
       PR: #179 and #180

## Single-node Validation
### Micro-benchmarks -- @guoshzhao @yukirora 

1. - [x] Memory (Tool: Nvidia Bandwidth Test Tool) -- @yukirora ETA: 5/28/2021 
   PR: #114  
     | Metrics | Unit | Description | 
     |---|---|---| 
     | H2D_Mem_BW_\<GPU ID> | GB/s | host-to-GPU bandwidth for each GPU |
     | D2H_Mem_BW_\<GPU ID> | GB/s | GPU-to-host bandwidth  for each GPU |

2. - [ ] Device P2P Bandwidth (Tool: Nvidia p2pBandwidthLatencyTest Tool) -- Delayed

     | Metrics | Unit | Description | 
     |---|---|---|
     | P2P_BW_Max | GB/s | The maximum bandwidth in Bidirectional P2P=Enabled Bandwidth Matrix for all GPUs |
     | P2P_BW_Min | GB/s | The minimum bandwidth |
     | P2P_BW_Avg | GB/s | The average bandwidth |
   
3. - [x] IBLoopback (Tool: PerfTest – Standard RDMA Test Tool) -- @yukirora  ETA: 7/30/2021 
    PR: #112 and #129 
     | Metrics | Unit | Description | 
     |---|---|---| 
     | IB_Write | MB/s | The IB write loopback throughput with different message size |
     | IB_Read | MB/s | The IB read loopback throughput with different message size  |
     | IB_Send | MB/s | The IB send loopback throughput with different message size  |

4. - [x] NCCL (Tool: Nvidia NCCL Test) -- @yukirora  ETA: 7/30/2021
    PR: #113 and #128
     | Metrics | Unit | Description | 
     |---|---|---|
     | NCCL_AllReduce | GB/s | The NCCL AllReduce performance with different message size |
     | NCCL_AllGather | GB/s | The NCCL AllGather performance with different message size |
     | NCCL_broadcast | GB/s | The NCCL Broadcast performance with different message size |
     | NCCL_reduce | GB/s | The NCCL Reduce performance with different message size |
     | NCCL_reduce_scatter | GB/s | The NCCL ReduceScatter performance with different message size |

5. - [x] Disk (Tool: FIO – Standard Disk Performance Tool) -- @yzygitzh ETA: 7/30/2021
   PR: #127 and #132 and #161
     | Metrics | Unit | Description | 
     |---|---|---|
     | Seq_Read | MB/s | Sequential read performance |
     | Seq_Write | MB/s | Sequential write performance |
     | Rand_Read | MB/s | Random read performance |
     | Rand_Write | MB/s | Random write performance |
     | Seq_R/W_Read | MB/s | Read performance in sequential read/write, fixed measurement (read:write = 4:1)|
     | Seq_R/W_Write | MB/s | Write performance in sequential read/write (read:write = 4:1)|
     | Rand_R/W_Read | MB/s | Read performance in random read/write (read:write = 4:1)|
     | Rand_R/W_Write | MB/s | Write performance in random read/write (read:write = 4:1)|

6. - [x] H2D/D2H SM Transmission Bandwidth (Tool: MSR-A build) -- @yzygitzh ETA: 8/6/2021
   PR: #162 and #169    
     | Metrics | Unit | Description | 
     |---|---|---|
     | H2D_SM_BW_\<GPU ID>| GB/s | host-to-GPU bandwidth using GPU kernel for each GPU |
     | D2H_SM_BW_\<GPU ID> | GB/s | GPU-to-host bandwidth using GPU kernel for each GPU  |

### 
## Support AMD

### Docker Image Support  -- @guoshzhao ETA: 7/16/2021

- [x] ROCm 4.2 PyTorch 1.7 PR: #164
- [x] ROCm 4.0 PyTorch 1.7 PR: #164

### Micro Benchmarks
1. - [x] Kernel Launch (Tool: MSR-A build) -- @yukirora  ETA: 7/30/2021
  PR: #137 and #136  
      | Metrics | Unit | Description |
      |---|---|---|
      | Kernel_Launch_Event_Time | Time (ms) | Dispatch latency measured in GPU time using hipEventRecord() |
      |Kernel_Launch_Wall_Time| Time (ms) | Dispatch latency measured in CPU time |

2. - [x] RCCL (Tool: AMD RCCL Test) -- @yukirora ETA: 7/30/2021
  PR: #139 and #143  
     | Metrics | Unit | Description | 
     |---|---|---|
     | RCCL_AllReduce | GB/s | The RCCL AllReduce performance with different message size |
     | RCCL_AllGather | GB/s | The RCCL AllGather performance with different message size |
     | RCCL_broadcast | GB/s | The RCCL Broadcast performance with different message size |
     | RCCL_reduce | GB/s | The RCCL Reduce performance with different message size |
     | RCCL_reduce_scatter | GB/s | The RCCL ReduceScatter performance with different message size |

3. - [x] GEMM FLOPS (Tool: AMD rocblas-bench Tool) -- @yukirora ETA: 8/27/2021
     PR: #144 and #165
     | Metrics | Unit | Description |
     |---|---|---|
     | FP64 | GFLOPS | FP64 FLOPS without MatrixCore  |
     | FP32 | GFLOPS | FP32 FLOPS without MatrixCore  |
     | FP16 | GFLOPS | FP16 FLOPS without MatrixCore  |
     | FP32(MC) | GFLOPS | TF32 FLOPS with MatrixCore  |
     | FP16(MC) | GFLOPS | FP16 FLOPS with MatrixCore  |
     | BF16(MC) | GFLOPS | BF16 FLOPS with MatrixCore  |
     | INT8(MC) | GOPS | INT8 FLOPS with MatrixCore  |
     | INT4(MC) | GOPS | INT4 FLOPS with MatrixCore  |

4. - [x] Memory (Tool: HIP Bandwidth Test Tool) -- @yukirora ETA: 8/27/2021 
   PR: #159 and #153
     | Metrics | Unit | Description | 
     |---|---|---| 
     | H2D_Mem_BW_\<GPU ID> | GB/s | host-to-GPU bandwidth for each GPU |
     | D2H_Mem_BW_\<GPU ID> | GB/s | GPU-to-host bandwidth  for each GPU |

### E2E Benchmarks -- @guoshzhao ETA: 7/16/2021
1. - [x] CNN models -- User PyTorch TORCHVISION.MODELS sub-package
      - ResNet: ResNet-50, ResNet-101, ResNet-152
      - DenseNet: DenseNet-169, DenseNet-201 ​
      - VGG: VGG-11, VGG-13, VGG-16, VGG-19​
2. - [x] BERT -- Use huggingface Transformers
      - BERT
      - BERT LARGE
3. - [x] LSTM -- Use PyTorch TORCH.NN sub-package
4. - [x] GPT-2 --  Use huggingface Transformers

## Result Summary -- @cp5555

- [x] Generate a report to summarize the results -- @guoshzhao  ETA: 7/30/2021
       PR: #147, #149, and #157
- [ ] Support basic analysis feature (boxplot figure, outlier detection, etc.) 

## Bug Fix
- [x] VGG models failed on A100 GPU with batch_size=128 #115 
       PR: #134 
## Other Improvement

1. Contribution related -- @lynex 
   - [x] Contribute rule (#131)
   - [x] system information collection (#160)

2. Document -- @TobeyQin 
    - [x] Add release process doc (#130)
    - [x] Add design documents (#125)
    - [x] Add developer guide doc for coding style (#155)
    - [x] Add contribution rules (#131)
    - [x] Add docker image list (#154)
    - [x] Add initial validation results
    - [x] ~~Add metric reasoning doc -- @cp5555 @guoshzhao~~

3. Process monitor
    - [ ] Add Heart beat to monitor process health
    - [ ] Auto kill all processes on all nodes

4. Coding style -- @abuccts 
    - [x] Add vscode online

------------
## Backlogs

### Multi-Node Benchmarks
- Mellanox ClusterKit
- GPCNeT

### UI Design

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `superbench/__init__.py`
Content:
```
1 # Copyright (c) Microsoft Corporation.
2 # Licensed under the MIT License.
3 
4 """SuperBench Python module.
5 
6 Provide hardware and software benchmarks for AI systems.
7 """
8 
9 __version__ = '0.2.1'
10 __author__ = 'Microsoft'
11
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/superbench/__init__.py b/superbench/__init__.py
--- a/superbench/__init__.py
+++ b/superbench/__init__.py
@@ -6,5 +6,5 @@
 Provide hardware and software benchmarks for AI systems.
 """
 
-__version__ = '0.2.1'
+__version__ = '0.3.0'
 __author__ = 'Microsoft'

{"golden_diff": "diff --git a/superbench/__init__.py b/superbench/__init__.py\n--- a/superbench/__init__.py\n+++ b/superbench/__init__.py\n@@ -6,5 +6,5 @@\n Provide hardware and software benchmarks for AI systems.\n \"\"\"\n \n-__version__ = '0.2.1'\n+__version__ = '0.3.0'\n __author__ = 'Microsoft'\n", "issue": "V0.3.0 Release Plan\n# Release Manager\r\n@TobeyQin \r\n\r\n# Endgame\r\nCode freeze: 9/1/2021\r\nBug Bash date: 9/2/2021\r\nRelease date: 9/17/2021\r\n\r\n# Main Features\r\n## SuperBench Framework\r\n\r\n### SB Runner -- @abuccts \r\n\r\n- [x] MPI mode implementation\r\n       PR: #146\r\n\r\n### SB Benchmarks -- @guoshzhao \r\n\r\n- [x] Docker Base\r\n       PR: #179 and #180\r\n\r\n## Single-node Validation\r\n### Micro-benchmarks -- @guoshzhao @yukirora \r\n\r\n1. - [x] Memory (Tool: Nvidia Bandwidth Test Tool) -- @yukirora ETA: 5/28/2021 \r\n   PR: #114  \r\n     | Metrics | Unit | Description | \r\n     |---|---|---| \r\n     | H2D_Mem_BW_\\<GPU ID> | GB/s | host-to-GPU bandwidth for each GPU |\r\n     | D2H_Mem_BW_\\<GPU ID> | GB/s | GPU-to-host bandwidth  for each GPU |\r\n\r\n2. - [ ] Device P2P Bandwidth (Tool: Nvidia p2pBandwidthLatencyTest Tool) -- Delayed\r\n\r\n     | Metrics | Unit | Description | \r\n     |---|---|---|\r\n     | P2P_BW_Max | GB/s | The maximum bandwidth in Bidirectional P2P=Enabled Bandwidth Matrix for all GPUs |\r\n     | P2P_BW_Min | GB/s | The minimum bandwidth |\r\n     | P2P_BW_Avg | GB/s | The average bandwidth |\r\n   \r\n3. - [x] IBLoopback (Tool: PerfTest \u2013 Standard RDMA Test Tool) -- @yukirora  ETA: 7/30/2021 \r\n    PR: #112 and #129 \r\n     | Metrics | Unit | Description | \r\n     |---|---|---| \r\n     | IB_Write | MB/s | The IB write loopback throughput with different message size |\r\n     | IB_Read | MB/s | The IB read loopback throughput with different message size  |\r\n     | IB_Send | MB/s | The IB send loopback throughput with different message size  |\r\n\r\n4. - [x] NCCL (Tool: Nvidia NCCL Test) -- @yukirora  ETA: 7/30/2021\r\n    PR: #113 and #128\r\n     | Metrics | Unit | Description | \r\n     |---|---|---|\r\n     | NCCL_AllReduce | GB/s | The NCCL AllReduce performance with different message size |\r\n     | NCCL_AllGather | GB/s | The NCCL AllGather performance with different message size |\r\n     | NCCL_broadcast | GB/s | The NCCL Broadcast performance with different message size |\r\n     | NCCL_reduce | GB/s | The NCCL Reduce performance with different message size |\r\n     | NCCL_reduce_scatter | GB/s | The NCCL ReduceScatter performance with different message size |\r\n\r\n5. - [x] Disk (Tool: FIO \u2013 Standard Disk Performance Tool) -- @yzygitzh ETA: 7/30/2021\r\n   PR: #127 and #132 and #161\r\n     | Metrics | Unit | Description | \r\n     |---|---|---|\r\n     | Seq_Read | MB/s | Sequential read performance |\r\n     | Seq_Write | MB/s | Sequential write performance |\r\n     | Rand_Read | MB/s | Random read performance |\r\n     | Rand_Write | MB/s | Random write performance |\r\n     | Seq_R/W_Read | MB/s | Read performance in sequential read/write, fixed measurement (read:write = 4:1)|\r\n     | Seq_R/W_Write | MB/s | Write performance in sequential read/write (read:write = 4:1)|\r\n     | Rand_R/W_Read | MB/s | Read performance in random read/write (read:write = 4:1)|\r\n     | Rand_R/W_Write | MB/s | Write performance in random read/write (read:write = 4:1)|\r\n\r\n6. - [x] H2D/D2H SM Transmission Bandwidth (Tool: MSR-A build) -- @yzygitzh ETA: 8/6/2021\r\n   PR: #162 and #169    \r\n     | Metrics | Unit | Description | \r\n     |---|---|---|\r\n     | H2D_SM_BW_\\<GPU ID>| GB/s | host-to-GPU bandwidth using GPU kernel for each GPU |\r\n     | D2H_SM_BW_\\<GPU ID> | GB/s | GPU-to-host bandwidth using GPU kernel for each GPU  |\r\n\r\n### \r\n## Support AMD\r\n\r\n### Docker Image Support  -- @guoshzhao ETA: 7/16/2021\r\n\r\n- [x] ROCm 4.2 PyTorch 1.7 PR: #164\r\n- [x] ROCm 4.0 PyTorch 1.7 PR: #164\r\n\r\n### Micro Benchmarks\r\n1. - [x] Kernel Launch (Tool: MSR-A build) -- @yukirora  ETA: 7/30/2021\r\n  PR: #137 and #136  \r\n      | Metrics | Unit | Description |\r\n      |---|---|---|\r\n      | Kernel_Launch_Event_Time | Time (ms) | Dispatch latency measured in GPU time using hipEventRecord() |\r\n      |Kernel_Launch_Wall_Time| Time (ms) | Dispatch latency measured in CPU time |\r\n\r\n2. - [x] RCCL (Tool: AMD RCCL Test) -- @yukirora ETA: 7/30/2021\r\n  PR: #139 and #143  \r\n     | Metrics | Unit | Description | \r\n     |---|---|---|\r\n     | RCCL_AllReduce | GB/s | The RCCL AllReduce performance with different message size |\r\n     | RCCL_AllGather | GB/s | The RCCL AllGather performance with different message size |\r\n     | RCCL_broadcast | GB/s | The RCCL Broadcast performance with different message size |\r\n     | RCCL_reduce | GB/s | The RCCL Reduce performance with different message size |\r\n     | RCCL_reduce_scatter | GB/s | The RCCL ReduceScatter performance with different message size |\r\n\r\n3. - [x] GEMM FLOPS (Tool: AMD rocblas-bench Tool) -- @yukirora ETA: 8/27/2021\r\n     PR: #144 and #165\r\n     | Metrics | Unit | Description |\r\n     |---|---|---|\r\n     | FP64 | GFLOPS | FP64 FLOPS without MatrixCore  |\r\n     | FP32 | GFLOPS | FP32 FLOPS without MatrixCore  |\r\n     | FP16 | GFLOPS | FP16 FLOPS without MatrixCore  |\r\n     | FP32(MC) | GFLOPS | TF32 FLOPS with MatrixCore  |\r\n     | FP16(MC) | GFLOPS | FP16 FLOPS with MatrixCore  |\r\n     | BF16(MC) | GFLOPS | BF16 FLOPS with MatrixCore  |\r\n     | INT8(MC) | GOPS | INT8 FLOPS with MatrixCore  |\r\n     | INT4(MC) | GOPS | INT4 FLOPS with MatrixCore  |\r\n\r\n4. - [x] Memory (Tool: HIP Bandwidth Test Tool) -- @yukirora ETA: 8/27/2021 \r\n   PR: #159 and #153\r\n     | Metrics | Unit | Description | \r\n     |---|---|---| \r\n     | H2D_Mem_BW_\\<GPU ID> | GB/s | host-to-GPU bandwidth for each GPU |\r\n     | D2H_Mem_BW_\\<GPU ID> | GB/s | GPU-to-host bandwidth  for each GPU |\r\n\r\n### E2E Benchmarks -- @guoshzhao ETA: 7/16/2021\r\n1. - [x] CNN models -- User PyTorch TORCHVISION.MODELS sub-package\r\n      - ResNet: ResNet-50, ResNet-101, ResNet-152\r\n      - DenseNet: DenseNet-169, DenseNet-201\u00a0\u200b\r\n      - VGG: VGG-11, VGG-13, VGG-16, VGG-19\u200b\r\n2. - [x] BERT -- Use huggingface Transformers\r\n      - BERT\r\n      - BERT LARGE\r\n3. - [x] LSTM -- Use PyTorch TORCH.NN sub-package\r\n4. - [x] GPT-2 --  Use huggingface Transformers\r\n\r\n## Result Summary -- @cp5555\r\n\r\n- [x] Generate a report to summarize the results -- @guoshzhao  ETA: 7/30/2021\r\n       PR: #147, #149, and #157\r\n- [ ] Support basic analysis feature (boxplot figure, outlier detection, etc.) \r\n\r\n## Bug Fix\r\n- [x] VGG models failed on A100 GPU with batch_size=128 #115 \r\n       PR: #134 \r\n## Other Improvement\r\n\r\n1. Contribution related -- @lynex \r\n   - [x] Contribute rule (#131)\r\n   - [x] system information collection (#160)\r\n\r\n2. Document -- @TobeyQin \r\n    - [x] Add release process doc (#130)\r\n    - [x] Add design documents (#125)\r\n    - [x] Add developer guide doc for coding style (#155)\r\n    - [x] Add contribution rules (#131)\r\n    - [x] Add docker image list (#154)\r\n    - [x] Add initial validation results\r\n    - [x] ~~Add metric reasoning doc -- @cp5555 @guoshzhao~~\r\n\r\n3. Process monitor\r\n    - [ ] Add Heart beat to monitor process health\r\n    - [ ] Auto kill all processes on all nodes\r\n\r\n4. Coding style -- @abuccts \r\n    - [x] Add vscode online\r\n\r\n------------\r\n## Backlogs\r\n\r\n### Multi-Node Benchmarks\r\n- Mellanox ClusterKit\r\n- GPCNeT\r\n\r\n### UI Design\n", "before_files": [{"content": "# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT License.\n\n\"\"\"SuperBench Python module.\n\nProvide hardware and software benchmarks for AI systems.\n\"\"\"\n\n__version__ = '0.2.1'\n__author__ = 'Microsoft'\n", "path": "superbench/__init__.py"}], "after_files": [{"content": "# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT License.\n\n\"\"\"SuperBench Python module.\n\nProvide hardware and software benchmarks for AI systems.\n\"\"\"\n\n__version__ = '0.3.0'\n__author__ = 'Microsoft'\n", "path": "superbench/__init__.py"}]}