[Bug] [RISC-V RVV] Performance Issue: log operator slower on RVV

[yanyanyanggg]

Issue: [RISC-V RVV] Performance Issue: log operator slower on RVV

Description

The log operator exhibits performance regression with the RISC‑V Vector (RVV) extension enabled. The acceleration ratio is 0.328, indicating the RVV implementation is approximately 3× slower than the scalar RV version. This is unexpected for an elementwise mathematical operation that should benefit from vectorization.

Steps to Reproduce

1. Generate the log operator with the following configuration:

params = {
    "dtype": "float32",
    "batch": 14,
    "channels": 23,
    "input_height": 67,
    "input_width": 99
}

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:

def export_log(params, set_dir=None, platform="rv"):
    data = relay.var("data",
                     shape=(params["batch"], params["channels"],
                            params["input_height"], params["input_width"]),
                     dtype=params["dtype"])
    log_op = relay.log(data)
    export_op(log_op, params["op_name"], [data], params, set_dir=set_dir)

Performance Data

• RV execution time: 13.393300 ms
• RVV execution time: 40.848000 ms
• Acceleration ratio (RV/RVV): 0.328 (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
• Input shape: (14, 23, 67, 99) ≈ 1.7M elements

Expected Behavior

RVV vectorization should provide a performance improvement over the scalar RV baseline for elementwise mathematical operations like log.

Additional Context

• The log operation is applied elementwise to a tensor of ~1.7M elements.
• This severe performance regression suggests inefficient vector code generation or suboptimal use of RVV instructions for mathematical functions.
• Similar regressions are observed across multiple operators (sum, relu, bias_add, sqrt, etc.), indicating a potential systemic issue with RVV vectorization in TVM.