Refactor environment variables

deep_gemm/utils.py

@@ -80,25 +80,10 @@ class suppress_stdout_stderr:
 def bench_kineto(fn, kernel_names, num_tests: int = 30, suppress_kineto_output: bool = False,
                  trace_path: str = None, barrier_comm_profiling: bool = False, flush_l2: bool = True):
     # Conflict with Nsight Systems
-    using_nsys = os.environ.get('DG_NSYS_PROFILING', False)
+    using_nsys = int(os.environ.get('DG_NSYS_PROFILING', 0))
 
     # By default, flush L2 with an excessive 8GB memset to give the GPU some (literal) chill time without full idle
-    # this avoid thermal throttling while keeping DVFS at max clocks (slight gain vs sleep / more consistent on GH200)
-    sleep_between_tests = 0.0
     flush_l2_size = int(8e9 // 4)
-    if os.environ.get('DG_BENCH_DISABLE_L2_FLUSH', False):
-        flush_l2 = False
-    if os.environ.get('DG_BENCH_POWER_LIMITED', False):
-        # if we want to be thermally limited, we need to run many iterations non-stop for a fairly long time
-        # and spend as little time as possible doing memset and other setup work (80MiB should be enough to flush L2)
-        num_tests = 2000
-        flush_l2_size = int(80e6 // 4)
-    sleep_val = os.environ.get('DG_BENCH_SLEEP_BETWEEN_TESTS', False)
-    if sleep_val:
-        try:
-            sleep_between_tests = float(sleep_val)
-        except ValueError:
-            pass  # Keep default
 
     # For some auto-tuning kernels with prints
     fn()
@@ -117,8 +102,6 @@ def bench_kineto(fn, kernel_names, num_tests: int = 30, suppress_kineto_output:
                     lhs @ rhs
                     dist.all_reduce(torch.ones(1, dtype=torch.float, device='cuda'))
                 for _ in range(num_tests):
-                    if sleep_between_tests > 0.0:
-                        time.sleep(sleep_between_tests)
                     if flush_l2:
                         torch.empty(flush_l2_size, dtype=torch.int, device='cuda').zero_()
                     fn()