adding fp32 strict and tf32x3 benchmarks for gemm

Summary:
TSIA

draft diff while I move this to its own op

Reviewed By: danzimm

Differential Revision: D64781204

fbshipit-source-id: c3ddd956230c1e4c8166867f03b5a28e8d6586e9

torchbenchmark/operators/gemm/operator.py

@@ -184,6 +184,24 @@ def triton_ops_matmul(self, a, b, bias) -> Callable:
             return lambda: kernels.matmul(a, b)
         return lambda: kernels.matmul(a, b) + bias
 
+    @register_benchmark(enabled=False, ci=False)
+    def triton_ops_with_tf32x23(self, a, b, bias) -> Callable:
+        input_precision = None
+        if self.dtype == torch.float32:
+            input_precision = "tf32x3"
+        if bias is None:
+            return lambda: kernels.matmul(a, b, None, input_precision)
+        return lambda: kernels.matmul(a, b, None, input_precision) + bias
+
+    @register_benchmark(enabled=False, ci=False)
+    def triton_ops_with_fp32_strict(self, a, b, bias) -> Callable:
+        input_precision = None
+        if self.dtype == torch.float32:
+            input_precision = "ieee"
+        if bias is None:
+            return lambda: kernels.matmul(a, b, self.dtype, input_precision)
+        return lambda: kernels.matmul(a, b, self.dtype, input_precision) + bias
+
     @register_benchmark(baseline=True)
     def aten_matmul(self, a, b, bias) -> Callable:
         if not bias == None: