yanyanyanggg opened a new issue, #18567:
URL: https://github.com/apache/tvm/issues/18567
### Issue: [RISC-V RVV] avg_pool2d operator shows performance degradation
#### Description
The average pooling operator (avg_pool2d) shows performance regression with
the RISC‑V Vector (RVV) extension, achieving only 0.621× the performance of the
scalar implementation. This suggests suboptimal vectorization for 2D pooling
operations.
#### Steps to Reproduce
1. Generate the avg_pool2d operator with the following configuration:
```python
params = {
"dtype": "float32",
"batch": 14,
"pool_channels": 23,
"pool_size": 2,
"stride": 4,
"padding": 1,
"input_height": 99,
"input_width": 95
}
```
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_avg_pool2d(params, set_dir=None, platform="rv"):
data = relay.var("data",
shape=(params["batch"], params["pool_channels"],
params["input_height"], params["input_width"]),
dtype=params["dtype"])
pool = relay.nn.avg_pool2d(
data,
pool_size=(params["pool_size"], params["pool_size"]),
strides=(params["stride"], params["stride"]),
padding=(params["padding"], params["padding"])
)
export_op(pool, params["op_name"], [data], params, set_dir=set_dir)
```
#### Performance Data
- **RV execution time**: 8.779250 ms
- **RVV execution time**: 14.134500 ms
- **Acceleration ratio (RV/RVV)**: 0.621 (RVV is ~1.6× 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
- **Operation**: 2×2 average pooling with stride 4 on input shape (14, 23,
99, 95)
#### Expected Behavior
RVV vectorization should provide a performance improvement over the scalar
RV baseline for 2D pooling operations like avg_pool2d.
#### Additional Context
- The operation performs 2×2 average pooling with stride 4 and padding 1 on
a 4D tensor.
- The performance regression indicates that the vectorized implementation of
2D pooling may have inefficient memory access patterns or suboptimal use of
vector instructions for reduction within pooling windows.
- This is part of a broader pattern where multiple operators show
performance degradation with RVV, suggesting potential issues with
vectorization strategies for 2D operations.
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