Benchmarks: micro benchmarks - add int8 support for cublaslt function (#574)

**Description**
add int8 support for cublaslt function.

superbench/benchmarks/micro_benchmarks/cublaslt_function.py

@@ -23,7 +23,7 @@ class CublasLtBenchmark(MicroBenchmarkWithInvoke):
         super().__init__(name, parameters)
 
         self._bin_name = 'cublaslt_gemm'
-        self._in_types = ['fp64', 'fp32', 'fp16', 'bf16', 'fp8e4m3', 'fp8e5m2']
+        self._in_types = ['fp64', 'fp32', 'fp16', 'bf16', 'fp8e4m3', 'fp8e5m2', 'int8']
 
     def mrange(self, start, stop=-1, multiplication_factor=2):
         """Range constructor with multiplication factor.

superbench/benchmarks/micro_benchmarks/cublaslt_gemm/cublaslt_gemm.cu

@@ -16,6 +16,7 @@ using fp16 = half;
 using bf16 = nv_bfloat16;
 using fp8e4m3 = __nv_fp8_e4m3;
 using fp8e5m2 = __nv_fp8_e5m2;
+using int8 = int8_t;
 
 struct Args {
     int m = 16;
@@ -84,6 +85,8 @@ template <typename T> cudaDataType_t get_datatype() {
         return CUDA_R_8F_E4M3;
     if (std::is_same<T, fp8e5m2>::value)
         return CUDA_R_8F_E5M2;
+    if (std::is_same<T, int8>::value)
+        return CUDA_R_8I;
     throw std::invalid_argument("Unknown type");
 }
 
@@ -162,6 +165,8 @@ int main(int argc, char **argv) {
         run<fp8e4m3, fp8e4m3, fp16>(&args);
     else if (args.in_type == "fp8e5m2")
         run<fp8e5m2, fp8e4m3, fp16>(&args);
+    else if (args.in_type == "int8")
+        run<int8>(&args);
     else
         throw std::invalid_argument("Unknown type " + args.in_type);
 

superbench/benchmarks/micro_benchmarks/cublaslt_gemm/cublaslt_utils.cc

@@ -62,6 +62,8 @@ void cublasLtGemm::Setup(int m, int n, int k, int batch, int lda, int ldb, int l
         gemm_compute_type = CUBLAS_COMPUTE_32F;
     if (a_type == CUDA_R_64F || b_type == CUDA_R_64F)
         gemm_compute_type = CUBLAS_COMPUTE_64F;
+    if (a_type == CUDA_R_8I)
+        gemm_compute_type = CUBLAS_COMPUTE_32I;
 
     cublasLtMatmulDesc_t op_desc = nullptr;
     CUBLAS_CHECK(cublasLtMatmulDescCreate(&op_desc, gemm_compute_type, CUDA_R_32F));

tests/benchmarks/micro_benchmarks/test_cublaslt_function.py

@@ -63,15 +63,15 @@ class CublasLtBenchmarkTestCase(BenchmarkTestCase, unittest.TestCase):
         (benchmark_cls, _) = BenchmarkRegistry._BenchmarkRegistry__select_benchmark(self.benchmark_name, Platform.CUDA)
         benchmark = benchmark_cls(
             self.benchmark_name,
-            parameters='--batch 2:16:2 --shapes 2:4,4:8,8:32 32:128:4,128,128 --in_types fp16 fp32 fp64',
+            parameters='--batch 2:16:2 --shapes 2:4,4:8,8:32 32:128:4,128,128 --in_types fp16 fp32 fp64 int8',
         )
         self.assertTrue(benchmark._preprocess())
-        self.assertEqual(4 * (2 * 2 * 3 + 2) * 3, len(benchmark._commands))
+        self.assertEqual(4 * (2 * 2 * 3 + 2) * len(benchmark._args.in_types), len(benchmark._commands))
 
         def cmd(t, b, m, n, k):
             return f'{benchmark._CublasLtBenchmark__bin_path} -m {m} -n {n} -k {k} -b {b} -w 20 -i 50 -t {t}'
 
-        for _t in ['fp16', 'fp32', 'fp64']:
+        for _t in ['fp16', 'fp32', 'fp64', 'int8']:
             for _b in [2, 4, 8, 16]:
                 for _m in [2, 4]:
                     for _n in [4, 8]: