run_rocbals_bench.sh

#!/bin/bash

# ROCm BLAS 性能测试脚本
# 测试多种矩阵形状和数据类型的GEMM性能

# 输出文件
export ROCBLAS_TENSILE_LIBPATH=./library_gpu5
export HIP_VISIBLE_DEVICES=3

chmod +x ${ROCM_PATH}/bin/rocblas-bench
OUTPUT_CSV="rocblas_bench_results_transB.csv"

# 写入CSV头
echo "shape_type,data_type,M,N,K,alpha,beta,transA,transB,a_type,b_type,c_type,d_type,compute_type,rocblas-Gflops,us" > ${OUTPUT_CSV}



# 定义测试函数
run_test() {
    local shape_type=$1
    local data_type=$2
    local M=$3
    local N=$4
    local K=$5
    local a_type=$6
    local b_type=$7
    local c_type=$8
    local d_type=$9
    local compute_type=${10}
    local alpha=${11}
    local beta=${12}
    local math_mode=${13}
    
    echo "Running test: ${shape_type} - ${data_type} - M=${M} N=${N} K=${K}"
    
    # 构建命令
    local cmd="rocblas-bench -f gemm_ex \
        -m ${M} -n ${N} -k ${K} \
        --alpha ${alpha} \
        --transposeA N --transposeB T \
        --a_type ${a_type} --lda ${M} \
        --b_type ${b_type} --ldb ${K} \
        --beta ${beta} \
        --c_type ${c_type} --ldc ${M} \
        --d_type ${d_type} --ldd ${M} \
        --compute_type ${compute_type} \
        --cold_iters 50 --iters 1000"
    
    # 如果指定了math_mode，则添加
    if [ -n "${math_mode}" ]; then
        cmd="${cmd} --math_mode ${math_mode}"
    fi
    
    # 运行rocblas-bench并捕获输出
    local output=$(eval ${cmd} 2>/dev/null | tail -1)
    
    # 解析输出
    if [ -n "${output}" ]; then
        # 提取rocblas-Gflops和us列
        local gflops=$(echo ${output} | awk '{print $(NF-1)}')
        local us=$(echo ${output} | awk '{print $NF}')
        echo "${shape_type},${data_type},${M},${N},${K},${alpha},${beta},N,N,${a_type},${b_type},${c_type},${d_type},${compute_type},${gflops},${us}" >> ${OUTPUT_CSV}
    else
        echo "Test failed or not supported: ${shape_type} - ${data_type}"
        echo "${shape_type},${data_type},${M},${N},${K},${alpha},${beta},N,N,${a_type},${b_type},${c_type},${d_type},${compute_type},FAILED,FAILED" >> ${OUTPUT_CSV}
    fi
}

# 测试场景3a: M,K,N同时相等的情况
for size in 128 256 512 1024 2048 4096 8192; do
    # fp64
    run_test "square" "fp64" ${size} ${size} ${size} "f64_r" "f64_r" "f64_r" "f64_r" "f64_r" 1.0 0.0 ""
    # fp32
    run_test "square" "fp32" ${size} ${size} ${size} "f32_r" "f32_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
    # fp16
    run_test "square" "fp16" ${size} ${size} ${size} "f16_r" "f16_r" "f16_r" "f16_r" "f32_r" 1.0 0.0 ""
    # bf16
    run_test "square" "bf16" ${size} ${size} ${size} "bf16_r" "bf16_r" "bf16_r" "bf16_r" "f32_r" 1.0 0.0 ""
    # tf32 - 使用f32_r类型和math_mode=1
    run_test "square" "tf32" ${size} ${size} ${size} "f32_r" "f32_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 "1"
done

# 测试场景3b: 非4对齐的情况
for size in 4098 8190; do
    # fp64
    run_test "non_aligned" "fp64" ${size} ${size} ${size} "f64_r" "f64_r" "f64_r" "f64_r" "f64_r" 1.0 0.0 ""
    # fp32
    run_test "non_aligned" "fp32" ${size} ${size} ${size} "f32_r" "f32_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
    # fp16
    run_test "non_aligned" "fp16" ${size} ${size} ${size} "f16_r" "f16_r" "f16_r" "f16_r" "f32_r" 1.0 0.0 ""
    # bf16
    run_test "non_aligned" "bf16" ${size} ${size} ${size} "bf16_r" "bf16_r" "bf16_r" "bf16_r" "f32_r" 1.0 0.0 ""
    # tf32 - 使用f32_r类型和math_mode=1
    run_test "non_aligned" "tf32" ${size} ${size} ${size} "f32_r" "f32_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 "1"
done

# 测试场景3c: M=8192, K=768, N=8192
run_test "special" "fp64" 8192 8192 768 "f64_r" "f64_r" "f64_r" "f64_r" "f64_r" 1.0 0.0 ""
run_test "special" "fp32" 8192 8192 768 "f32_r" "f32_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
run_test "special" "fp16" 8192 8192 768 "f16_r" "f16_r" "f16_r" "f16_r" "f32_r" 1.0 0.0 ""
run_test "special" "bf16" 8192 8192 768 "bf16_r" "bf16_r" "bf16_r" "bf16_r" "f32_r" 1.0 0.0 ""
run_test "special" "tf32" 8192 8192 768 "f32_r" "f32_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 "1"

# 混合精度测试
for size in 128 256 512 1024 2048 4096 8192; do
    # fp16_fp32
    run_test "mixed" "fp16_fp32" ${size} ${size} ${size} "f16_r" "f16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
    # bf16_fp32
    run_test "mixed" "bf16_fp32" ${size} ${size} ${size} "bf16_r" "bf16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
    # int8_int32
    run_test "mixed" "int8_int32" ${size} ${size} ${size} "i8_r" "i8_r" "i32_r" "i32_r" "i32_r" 1.0 0.0 ""
done

for size in 4098 8190; do
    # fp16_fp32
    run_test "mixed" "fp16_fp32" ${size} ${size} ${size} "f16_r" "f16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
    # bf16_fp32
    run_test "mixed" "bf16_fp32" ${size} ${size} ${size} "bf16_r" "bf16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
    # int8_int32
    run_test "mixed" "int8_int32" ${size} ${size} ${size} "i8_r" "i8_r" "i32_r" "i32_r" "i32_r" 1.0 0.0 ""
done


# 场景5b: A,B: fp16, C,D: fp32
run_test "mixed" "fp16_fp32" 4096 4096 4096 "f16_r" "f16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
run_test "mixed" "fp16_fp32" 8192 8192 8192 "f16_r" "f16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
run_test "mixed" "fp16_fp32" 8192 8192 768 "f16_r" "f16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""

# 场景5c: A,B: bf16, C,D: fp32
run_test "mixed" "bf16_fp32" 4096 4096 4096 "bf16_r" "bf16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
run_test "mixed" "bf16_fp32" 8192 8192 8192 "bf16_r" "bf16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""
run_test "mixed" "bf16_fp32" 8192 8192 768 "bf16_r" "bf16_r" "f32_r" "f32_r" "f32_r" 1.0 0.0 ""

# 场景5e: A,B: int8, C,D: int32
run_test "mixed" "int8_int32" 4096 4096 4096 "i8_r" "i8_r" "i32_r" "i32_r" "i32_r" 1.0 0.0 ""
run_test "mixed" "int8_int32" 8192 8192 8192 "i8_r" "i8_r" "i32_r" "i32_r" "i32_r" 1.0 0.0 ""
run_test "mixed" "int8_int32" 8192 8192 768 "i8_r" "i8_r" "i32_r" "i32_r" "i32_r" 1.0 0.0 ""