[Bug] Issue: [RISC-V RVV] negative operator shows performance degradation

[yanyanyanggg]

Issue: [RISC-V RVV] negative operator shows performance degradation

Description

The negative operator (elementwise negation) shows performance regression with the RISC‑V Vector (RVV) extension, achieving only 0.854× the performance of the scalar implementation. This is unexpected for a simple arithmetic operation that should benefit from vectorization.

Steps to Reproduce

1. Generate the negative 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_negative(params, set_dir=None, platform="rv"):
    data = relay.var("data",
                     shape=(params["batch"], params["channels"],
                            params["input_height"], params["input_width"]),
                     dtype=params["dtype"])
    neg_op = relay.negative(data)
    export_op(neg_op, params["op_name"], [data], params, set_dir=set_dir)

Performance Data

• RV execution time: 7.581020 ms
• RVV execution time: 8.875480 ms
• Acceleration ratio (RV/RVV): 0.854 (RVV is ~1.17× 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: Elementwise negation on ~1.7M elements

Expected Behavior

RVV vectorization should provide a performance improvement over the scalar RV baseline for simple arithmetic operations like negation.

Additional Context

• The negative operation is applied elementwise to a tensor of ~1.7M elements.
• The performance regression, while less severe than other operators, is still unexpected and indicates that even simple arithmetic operations are not being efficiently vectorized.
• This issue is part of a broader pattern where all tested operators show performance degradation with RVV, suggesting a potential systemic issue in TVM's RVV code generation or optimization.