yanyanyanggg opened a new issue, #18560:
URL: https://github.com/apache/tvm/issues/18560
### Issue: [RISC-V RVV] Performance Regression: sum operator slower on RVV
than RV
#### Description
The sum operator shows significant performance degradation when using the
RISC‑V Vector (RVV) extension compared to the scalar RV baseline. The
acceleration ratio is 0.325, meaning the RVV version is about 3× slower. This
is unexpected because vector extensions should improve performance, especially
for reduction operations like sum.
#### Steps to Reproduce
1. Generate the sum operator with the following configuration:
```python
params = {
"dtype": "float32",
"batch": 14,
"channels": 23,
"input_height": 67,
"input_width": 99,
"axis": 1,
"keepdims": True
}
```
2. Export the operator to two targets:
- **RV target** (scalar, without vector extension):
```
llvm -mtriple=riscv64-linux-gnu -mcpu=generic-rv64 -mabi=lp64d
-mattr=+64bit,+m,+a,+f,+d,+c
```
- **RVV target** (with vector extension):
```
llvm -mtriple=riscv64-linux-gnu -mcpu=generic-rv64 -mabi=lp64d
-mattr=+64bit,+m,+a,+f,+d,+c,+v
```
3. Run performance measurement on both targets.
Operator definition code:
```python
def export_sum(params, set_dir=None, platform="rv"):
data = relay.var("data",
shape=(params["batch"], params["channels"],
params["input_height"], params["input_width"]),
dtype=params["dtype"])
sum_op = relay.sum(data, axis=params["axis"],
keepdims=params["keepdims"])
export_op(sum_op, params["op_name"], [data], params, set_dir=set_dir)
```
#### Performance Data
- **RV execution time**: 9.301150 ms
- **RVV execution time**: 28.622800 ms
- **Acceleration ratio (RV/RVV)**: 0.325 (RVV is ~3× slower)
#### Environment Information
- **TVM version**: 0.19.0
- **LLVM version**: [Please provide: `llvm-config --version`]
- **Hardware**: Spacemit K1‑X bit‑brick board
- **CPU**: Spacemit X60 (8 cores, 1.6 GHz)
- **ISA**: rv64imafdcv (with vector extensions)
- **Memory**: 7.6 GB
- **OS**: Bianbu 2.2, Linux kernel 6.6.63
#### Expected Behavior
RVV vectorization should provide a performance improvement over the scalar
RV baseline for reduction operations like sum.
#### Additional Context
- The sum operation reduces along axis=1 on a tensor of shape (14, 23, 67,
99) (≈1.7M elements).
- The performance regression suggests suboptimal vectorization for reduction
operations on RVV.
- Other operators (log, relu, bias_add, sqrt, etc.) also show similar
regressions, indicating a broader RVV code‑generation or optimization issue.
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