[Bug] [RISC-V RVV] Performance Issue: bias_add operator slower with vectorization

[yanyanyanggg]

Issue: [RISC-V RVV] Performance Issue: bias_add operator slower with vectorization

Description

The bias_add operator shows significant performance degradation when using the RISC‑V Vector (RVV) extension. With an acceleration ratio of 0.360, the RVV implementation is nearly 3× slower than the scalar implementation. This is unexpected for a channel‑wise addition operation that should benefit from vectorization.

Steps to Reproduce

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

Performance Data

• RV execution time: 7.683920 ms
• RVV execution time: 21.363800 ms
• Acceleration ratio (RV/RVV): 0.360 (RVV is ~2.8× 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: Channel‑wise bias addition on a tensor of shape (14, 23, 67, 99)

Expected Behavior

RVV vectorization should provide a performance improvement over the scalar RV baseline for broadcast addition operations like bias_add.

Additional Context

• The bias_add operation adds a 1D bias vector to each channel of a 4D tensor (≈1.7M elements total).
• The performance regression is severe and similar to other operators (sum, log, relu, etc.).
• This suggests that the current RVV vectorization for broadcast operations may be suboptimal, or there are inefficiencies in memory access patterns or instruction selection.