reland #96248 [inductor] show performance for each autotune config fo…

…r a kernel (#96458)

Pull Request resolved: #96458
Approved by: https://github.com/ngimel

torch/_inductor/codegen/triton.py

@@ -1205,8 +1205,9 @@ def codegen_kernel_benchmark(self):
         result.writelines(["\n", "\n", "def call(args):"])
         grid = []
         extra_args = []
+        extra_args_str = None
+        index = V.graph.scheduler.current_device.index
         with result.indent():
-            index = V.graph.scheduler.current_device.index
             result.writeline(f"with torch.cuda._DeviceGuard({index}):")
             with result.indent():
                 result.writeline(
@@ -1226,6 +1227,18 @@ def codegen_kernel_benchmark(self):
                     f"triton_.run(*args, {extra_args_str}grid=grid({', '.join(grid)}), stream={stream_name})"
                 )
 
+        # benchmark all configs
+        result.writelines(["\n", "\n", "def benchmark_all_configs(args):"])
+        with result.indent():
+            result.writeline(f"with torch.cuda._DeviceGuard({index}):")
+            with result.indent():
+                result.writeline(
+                    f"torch.cuda.set_device({index})"
+                )  # no-op to ensure context
+                result.writeline(
+                    f"return triton_.benchmark_all_configs(*args, {extra_args_str}grid=grid({', '.join(grid)}))"
+                )
+
         result.writelines(["\n", "\n", "if __name__ == '__main__':"])
         with result.indent():
             result.writeline("from torch._inductor.utils import get_num_bytes")

torch/_inductor/codegen/wrapper.py

@@ -614,13 +614,16 @@ def add_benchmark_harness(self, output):
                     "",
                     "parser = argparse.ArgumentParser()",
                     'parser.add_argument("--benchmark-kernels", "-k", action="store_true", help="Whether to benchmark each individual kernels")',  # noqa: B950, line too long
+                    'parser.add_argument("--benchmark-all-configs", "-c", action="store_true", help="Whether to benchmark each individual config for a kernel")',  # noqa: B950, line too long
                     "args = parser.parse_args()",
                     "",
                     "if args.benchmark_kernels:",
                 ]
             )
             with output.indent():
-                output.writeline(f"benchmark_all_kernels('{get_benchmark_name()}')")
+                output.writeline(
+                    f"benchmark_all_kernels('{get_benchmark_name()}', args.benchmark_all_configs)"
+                )
             output.writeline("else:")
             with output.indent():
                 output.writeline("benchmark_compiled_module()")

torch/_inductor/config.py

@@ -90,16 +90,29 @@ def is_fbcode():
 # warnings intended for PyTorch developers, disable for point releases
 developer_warnings = is_fbcode() or "+" in torch.__version__
 
-compile_threads = (
-    1
-    if sys.platform == "win32" or is_fbcode()
-    else min(
-        32,
-        len(os.sched_getaffinity(0))
-        if hasattr(os, "sched_getaffinity")
-        else os.cpu_count(),
-    )
-)
+
+def decide_compile_threads():
+    """
+    Here are the precedence to decide compile_threads
+    1. User can override it by TORCHINDUCTOR_COMPILE_THREADS.  One may want to disable async compiling by
+       setting this to 1 to make pdb happy.
+    2. Set to 1 if it's win32 platform or it's a fbcode build
+    3. decide by the number of CPU cores
+    """
+    if "TORCHINDUCTOR_COMPILE_THREADS" in os.environ:
+        return int(os.environ["TORCHINDUCTOR_COMPILE_THREADS"])
+    elif sys.platform == "win32" or is_fbcode():
+        return 1
+    else:
+        return min(
+            32,
+            len(os.sched_getaffinity(0))
+            if hasattr(os, "sched_getaffinity")
+            else os.cpu_count(),
+        )
+
+
+compile_threads = decide_compile_threads()
 
 # autotuning global cache path
 if is_fbcode():

torch/_inductor/triton_ops/autotune.py

@@ -155,8 +155,7 @@ def kernel_call():
         return do_bench(kernel_call, rep=40, fast_flush=True)
 
     @dynamo_timed
-    def autotune_to_one_config(self, *args, **kwargs):
-        """Do the actual autotuning"""
+    def benchmark_all_configs(self, *args, **kwargs):
         from ..compile_fx import clone_preserve_strides
 
         # clone inplace buffers to avoid autotune contaminating them if
@@ -171,9 +170,14 @@ def autotune_to_one_config(self, *args, **kwargs):
                 cloned_args.append(arg)
 
         timings = {
-            launcher: self.bench(launcher, *cloned_args, **kwargs)
+            launcher: self.bench(launcher, *cloned_args, **kwargs)[0]
             for launcher in self.launchers
         }
+        return timings
+
+    def autotune_to_one_config(self, *args, **kwargs):
+        """Do the actual autotuning"""
+        timings = self.benchmark_all_configs(*args, **kwargs)
         self.launchers = [builtins.min(timings, key=timings.get)]
         if self.save_cache_hook:
             self.save_cache_hook(self.launchers[0].config)
@@ -313,8 +317,13 @@ def cached_autotune(
     configs = unique_configs(configs)
     assert len(configs) == 1 or filename
 
+    # The autotune cache will simply replace the list of candidate configs with
+    # the best config cached. We don't want that when we benchmark triton kernels.
+    # We need the perf for each of the candidate config instead.
+    cache_autotune_result = not config.benchmark_kernel
+
     # on disk caching logic
-    if filename is not None and len(configs) > 1:
+    if cache_autotune_result and filename is not None and len(configs) > 1:
         cache_filename = os.path.splitext(filename)[0] + ".best_config"
         configs_hash = hash_configs(configs)
         best_config = load_cached_autotuning(cache_filename, configs_hash, configs)

torch/_inductor/utils.py

@@ -625,7 +625,7 @@ def get_benchmark_name():
             return arg[len("--only=") :]
 
 
-def benchmark_all_kernels(benchmark_name):
+def benchmark_all_kernels(benchmark_name, benchmark_all_configs):
     """
     An experimental API used only when config.benchmark_kernel is true.
 
@@ -642,18 +642,34 @@ def benchmark_all_kernels(benchmark_name):
         if not hasattr(kernel_mod, "get_args") or not hasattr(kernel_mod, "call"):
             continue
         args = kernel_mod.get_args()
-        ms = do_bench(lambda: kernel_mod.call(args), rep=40, fast_flush=True)[0]
         num_gb = get_num_bytes(*args) / 1e9
-        gb_per_s = num_gb / (ms / 1e3)
 
-        # follow what we do in DebugAutotuner
-        info_str = f"{benchmark_name:20} {kernel_key[:10]} {ms:.3f}ms    {num_gb:.3f}GB    {gb_per_s:.2f}GB/s"
-        import colorama
-
-        if ms > 0.012 and gb_per_s < 650:
-            print(colorama.Fore.RED + info_str + colorama.Fore.RESET)
+        def get_info_str(ms, prefix=""):
+            gb_per_s = num_gb / (ms / 1e3)
+            # follow what we do in DebugAutotuner
+            info_str = f"{prefix}{ms:.3f}ms    {num_gb:.3f}GB    {gb_per_s:.2f}GB/s"
+            import colorama
+
+            if ms > 0.012 and gb_per_s < 650:
+                info_str = colorama.Fore.RED + info_str + colorama.Fore.RESET
+            return info_str
+
+        bench_result = []
+        if benchmark_all_configs:
+            assert hasattr(kernel_mod, "benchmark_all_configs")
+            bench_result = kernel_mod.benchmark_all_configs(args)
+            bench_result = [
+                (launcher.config, ms) for launcher, ms in bench_result.items()
+            ]
+            print(f"{benchmark_name:20} {kernel_key[:10]}")
+            for cfg, ms in bench_result:
+                print(f"  {get_info_str(ms)} @ {cfg}")
         else:
-            print(info_str)
+            ms = do_bench(lambda: kernel_mod.call(args), rep=40, fast_flush=True)[0]
+            assert (
+                len(kernel_mod.triton_.launchers) == 1
+            ), "Autotuner should have selected the best config"
+            print(get_info_str(ms, prefix=f"{benchmark_name:20} {kernel_key[:10]} "))
 
         nfound += 1
     if nfound == 0: