[MetaSchedule][Runtime] Enhance Runner RandomFill

CMakeLists.txt

@@ -89,6 +89,7 @@ tvm_option(USE_CUDNN "Build with cuDNN" OFF)
 tvm_option(USE_CUBLAS "Build with cuBLAS" OFF)
 tvm_option(USE_CUTLASS "Build with CUTLASS" OFF)
 tvm_option(USE_THRUST "Build with Thrust" OFF)
+tvm_option(USE_CURAND "Build with cuRAND" OFF)
 tvm_option(USE_MIOPEN "Build with ROCM:MIOpen" OFF)
 tvm_option(USE_ROCBLAS "Build with ROCM:RoCBLAS" OFF)
 tvm_option(USE_SORT "Build with sort support" ON)

cmake/config.cmake

@@ -296,6 +296,9 @@ set(USE_VTA_FPGA OFF)
 # Whether use Thrust
 set(USE_THRUST OFF)
 
+# Whether use cuRAND
+set(USE_CURAND OFF)
+
 # Whether to build the TensorFlow TVMDSOOp module
 set(USE_TF_TVMDSOOP OFF)
 

cmake/modules/CUDA.cmake

@@ -69,6 +69,18 @@ if(USE_CUDA)
     list(APPEND RUNTIME_SRCS ${CONTRIB_THRUST_SRC})
   endif(USE_THRUST)
 
+  if(USE_CURAND)
+    message(STATUS "Build with cuRAND support")
+    message(STATUS "${CUDA_CURAND_LIBRARY}")
+    cmake_minimum_required(VERSION 3.13) # to compile CUDA code
+    enable_language(CUDA)
+    tvm_file_glob(GLOB CONTRIB_CURAND_SRC_CC src/runtime/contrib/curand/*.cc)
+    tvm_file_glob(GLOB CONTRIB_CURAND_SRC_CU src/runtime/contrib/curand/*.cu)
+    list(APPEND TVM_RUNTIME_LINKER_LIBS ${CUDA_CURAND_LIBRARY})
+    list(APPEND RUNTIME_SRCS ${CONTRIB_CURAND_SRC_CC})
+    list(APPEND RUNTIME_SRCS ${CONTRIB_CURAND_SRC_CU})
+  endif(USE_CURAND)
+
   if(USE_GRAPH_EXECUTOR_CUDA_GRAPH)
     if(NOT USE_GRAPH_EXECUTOR)
       message(FATAL_ERROR "CUDA Graph is only supported by graph executor, please set USE_GRAPH_EXECUTOR=ON")

cmake/modules/LibInfo.cmake

@@ -111,6 +111,7 @@ function(add_lib_info src_file)
     TVM_INFO_USE_TFLITE="${USE_TFLITE}"
     TVM_INFO_USE_THREADS="${USE_THREADS}"
     TVM_INFO_USE_THRUST="${USE_THRUST}"
+    TVM_INFO_USE_CURAND="${USE_CURAND}"
     TVM_INFO_USE_VITIS_AI="${USE_VITIS_AI}"
     TVM_INFO_USE_VULKAN="${USE_VULKAN}"
     TVM_INFO_USE_CLML="${USE_CLML}"

cmake/utils/FindCUDA.cmake

@@ -85,6 +85,10 @@ macro(find_cuda use_cuda use_cudnn)
         PATHS ${CUDA_TOOLKIT_ROOT_DIR}
         PATH_SUFFIXES lib lib64 targets/x86_64-linux/lib targets/x86_64-linux/lib/stubs lib64/stubs lib/x86_64-linux-gnu
         NO_DEFAULT_PATH)
+      find_library(CUDA_CURAND_LIBRARY curand
+        ${CUDA_TOOLKIT_ROOT_DIR}/lib64
+        ${CUDA_TOOLKIT_ROOT_DIR}/lib
+        NO_DEFAULT_PATH)
       find_library(CUDA_CUBLAS_LIBRARY cublas
         ${CUDA_TOOLKIT_ROOT_DIR}/lib64
         ${CUDA_TOOLKIT_ROOT_DIR}/lib
@@ -134,6 +138,7 @@ macro(find_cuda use_cuda use_cudnn)
     message(STATUS "Found CUDA_CUDNN_INCLUDE_DIRS=" ${CUDA_CUDNN_INCLUDE_DIRS})
     message(STATUS "Found CUDA_CUDNN_LIBRARY=" ${CUDA_CUDNN_LIBRARY})
     message(STATUS "Found CUDA_CUBLAS_LIBRARY=" ${CUDA_CUBLAS_LIBRARY})
+    message(STATUS "Found CUDA_CURAND_LIBRARY=" ${CUDA_CURAND_LIBRARY})
     message(STATUS "Found CUDA_CUBLASLT_LIBRARY=" ${CUDA_CUBLASLT_LIBRARY})
   endif(CUDA_FOUND)
 endmacro(find_cuda)

docs/contribute/pull_request.rst

@@ -118,6 +118,7 @@ space. You can remove stale images that aren't used in the presently checked-out
 other worktrees using the following command:
 
 .. code:: bash
+
     docker/clear-stale-images.sh
 
 Consult the ``--help`` for more options.

python/tvm/auto_scheduler/testing/tune_onnx.py

@@ -26,6 +26,7 @@
 from tvm import meta_schedule as ms
 from tvm import relay
 from tvm.meta_schedule.testing.custom_builder_runner import run_module_via_rpc
+from tvm.meta_schedule.utils import cpu_count
 from tvm.relay.frontend import from_onnx
 from tvm.support import describe
 
@@ -73,11 +74,6 @@ def _parse_args():
         type=str,
         required=True,
     )
-    args.add_argument(
-        "--rpc-workers",
-        type=int,
-        required=True,
-    )
     args.add_argument(
         "--work-dir",
         type=str,
@@ -100,7 +96,7 @@ def _parse_args():
     )
     args.add_argument(
         "--cpu-flush",
-        type=bool,
+        type=int,
         required=True,
     )
     parsed = args.parse_args()
@@ -125,7 +121,7 @@ def main():
         key=ARGS.rpc_key,
         host=ARGS.rpc_host,
         port=ARGS.rpc_port,
-        n_parallel=ARGS.rpc_workers,
+        n_parallel=cpu_count(logical=True),
         number=ARGS.number,
         repeat=ARGS.repeat,
         min_repeat_ms=ARGS.min_repeat_ms,

python/tvm/auto_scheduler/testing/tune_relay.py

@@ -26,6 +26,7 @@
 from tvm import relay
 from tvm.meta_schedule.testing.custom_builder_runner import run_module_via_rpc
 from tvm.meta_schedule.testing.relay_workload import get_network
+from tvm.meta_schedule.utils import cpu_count
 from tvm.support import describe
 
 
@@ -66,11 +67,6 @@ def _parse_args():
         type=str,
         required=True,
     )
-    args.add_argument(
-        "--rpc-workers",
-        type=int,
-        required=True,
-    )
     args.add_argument(
         "--work-dir",
         type=str,
@@ -98,7 +94,7 @@ def _parse_args():
     )
     args.add_argument(
         "--cpu-flush",
-        type=bool,
+        type=int,
         required=True,
     )
     parsed = args.parse_args()
@@ -123,7 +119,7 @@ def main():
         key=ARGS.rpc_key,
         host=ARGS.rpc_host,
         port=ARGS.rpc_port,
-        n_parallel=ARGS.rpc_workers,
+        n_parallel=cpu_count(logical=True),
         number=ARGS.number,
         repeat=ARGS.repeat,
         min_repeat_ms=ARGS.min_repeat_ms,

python/tvm/auto_scheduler/testing/tune_te.py

@@ -21,6 +21,7 @@
 import tvm
 from tvm import auto_scheduler
 from tvm.meta_schedule.testing.te_workload import CONFIGS
+from tvm.meta_schedule.utils import cpu_count
 from tvm.support import describe
 
 
@@ -56,11 +57,6 @@ def _parse_args():
         type=str,
         required=True,
     )
-    args.add_argument(
-        "--rpc-workers",
-        type=int,
-        required=True,
-    )
     args.add_argument(
         "--work-dir",
         type=str,
@@ -83,7 +79,7 @@ def _parse_args():
     )
     args.add_argument(
         "--cpu-flush",
-        type=bool,
+        type=int,
         required=True,
     )
     parsed = args.parse_args()
@@ -132,7 +128,7 @@ def main():
         key=ARGS.rpc_key,
         host=ARGS.rpc_host,
         port=ARGS.rpc_port,
-        n_parallel=ARGS.rpc_workers,
+        n_parallel=cpu_count(logical=True),
         number=ARGS.number,
         repeat=ARGS.repeat,
         min_repeat_ms=ARGS.min_repeat_ms,

python/tvm/meta_schedule/runner/local_runner.py

@@ -23,17 +23,17 @@
 
 from ...contrib.popen_pool import PopenPoolExecutor
 from ...runtime import Device, Module
+from ..profiler import Profiler
 from ..utils import derived_object, get_global_func_with_default_on_worker
 from .config import EvaluatorConfig
-from .runner import PyRunner, RunnerFuture, RunnerInput, RunnerResult, PyRunnerFuture
+from .runner import PyRunner, PyRunnerFuture, RunnerFuture, RunnerInput, RunnerResult
 from .utils import (
-    T_ARGUMENT_LIST,
     T_ARG_INFO_JSON_OBJ_LIST,
+    T_ARGUMENT_LIST,
     alloc_argument_common,
     run_evaluator_common,
 )
 
-
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
 
@@ -137,26 +137,29 @@ def resource_handler():
             yield
         finally:
             # Final step. Always clean up
-            f_cleanup()
+            with Profiler.timeit("LocalRunner/cleanup"):
+                f_cleanup()
 
     with resource_handler():
         # Step 1: create the local runtime module
-        rt_mod = tvm.runtime.load_module(artifact_path)
-        # Step 2: create the local device
-        device = tvm.runtime.device(dev_type=device_type, dev_id=0)
-        # Step 3: Allocate input arguments
-        repeated_args: List[T_ARGUMENT_LIST] = f_alloc_argument(
-            device,
-            args_info,
-            alloc_repeat,
-        )
-        # Step 4: Run time_evaluator
-        costs: List[float] = f_run_evaluator(
-            rt_mod,
-            device,
-            evaluator_config,
-            repeated_args,
-        )
+        with Profiler.timeit("LocalRunner/load_module"):
+            rt_mod = tvm.runtime.load_module(artifact_path)
+        # Step 2: Allocate input arguments
+        with Profiler.timeit("LocalRunner/alloc_argument"):
+            device = tvm.runtime.device(dev_type=device_type, dev_id=0)
+            repeated_args: List[T_ARGUMENT_LIST] = f_alloc_argument(
+                device,
+                args_info,
+                alloc_repeat,
+            )
+        # Step 3: Run time_evaluator
+        with Profiler.timeit("LocalRunner/run_evaluator"):
+            costs: List[float] = f_run_evaluator(
+                rt_mod,
+                device,
+                evaluator_config,
+                repeated_args,
+            )
     return costs
 
 
@@ -313,9 +316,6 @@ def _check(
             get_global_func_with_default_on_worker(name=f_alloc_argument, default=None)
             get_global_func_with_default_on_worker(name=f_run_evaluator, default=None)
             get_global_func_with_default_on_worker(name=f_cleanup, default=None)
-            get_global_func_with_default_on_worker(
-                name="tvm.contrib.random.random_fill", default=None
-            )
 
         value = self.pool.submit(
             _check,
@@ -348,7 +348,7 @@ def default_alloc_argument(
         The allocation args
     """
     f_random_fill = get_global_func_with_default_on_worker(
-        name="tvm.contrib.random.random_fill", default=None
+        name="tvm.contrib.random.random_fill_for_measure", default=None
     )
     return alloc_argument_common(f_random_fill, device, args_info, alloc_repeat)
 

python/tvm/meta_schedule/runner/rpc_runner.py

@@ -25,6 +25,7 @@
 from tvm.rpc import RPCSession
 from tvm.runtime import Device, Module
 
+from ..profiler import Profiler
 from ..utils import (
     cpu_count,
     derived_object,
@@ -243,7 +244,7 @@ def __init__(
         f_alloc_argument: Union[T_ALLOC_ARGUMENT, str, None] = None,
         f_run_evaluator: Union[T_RUN_EVALUATOR, str, None] = None,
         f_cleanup: Union[T_CLEANUP, str, None] = None,
-        max_workers: Optional[int] = 1,
+        max_workers: Optional[int] = None,
         initializer: Optional[Callable[[], None]] = None,
     ) -> None:
         """Constructor
@@ -284,7 +285,7 @@ def __init__(
         self.f_run_evaluator = f_run_evaluator
         self.f_cleanup = f_cleanup
         if max_workers is None:
-            max_workers = cpu_count()
+            max_workers = cpu_count(logical=True)
         logger.info("RPCRunner: max_workers = %d", max_workers)
         self.pool = PopenPoolExecutor(
             max_workers=max_workers,
@@ -378,31 +379,36 @@ def resource_handler():
             yield
         finally:
             # Final step. Always clean up
-            f_cleanup(session, remote_path)
+            with Profiler.timeit("RPCRunner/cleanup"):
+                f_cleanup(session, remote_path)
 
     with resource_handler():
         # Step 1. Create session
-        session = f_create_session(rpc_config)
-        device = session.device(dev_type=device_type, dev_id=0)
+        with Profiler.timeit("RPCRunner/create_session"):
+            session = f_create_session(rpc_config)
+            device = session.device(dev_type=device_type, dev_id=0)
         # Step 2. Upload the module
-        _, remote_path = osp.split(artifact_path)
-        local_path: str = artifact_path
-        rt_mod: Module = f_upload_module(session, local_path, remote_path)
+        with Profiler.timeit("RPCRunner/upload_module"):
+            _, remote_path = osp.split(artifact_path)
+            local_path: str = artifact_path
+            rt_mod: Module = f_upload_module(session, local_path, remote_path)
         # Step 3: Allocate input arguments
-        repeated_args: List[T_ARGUMENT_LIST] = f_alloc_argument(
-            session,
-            device,
-            args_info,
-            alloc_repeat,
-        )
+        with Profiler.timeit("RPCRunner/alloc_argument"):
+            repeated_args: List[T_ARGUMENT_LIST] = f_alloc_argument(
+                session,
+                device,
+                args_info,
+                alloc_repeat,
+            )
         # Step 4: Run time_evaluator
-        costs: List[float] = f_run_evaluator(
-            session,
-            rt_mod,
-            device,
-            evaluator_config,
-            repeated_args,
-        )
+        with Profiler.timeit("LocalRunner/run_evaluator"):
+            costs: List[float] = f_run_evaluator(
+                session,
+                rt_mod,
+                device,
+                evaluator_config,
+                repeated_args,
+            )
     return costs
 
 
@@ -474,7 +480,7 @@ def default_alloc_argument(
     """
     f_random_fill = get_global_func_on_rpc_session(
         session,
-        "tvm.contrib.random.random_fill",
+        "tvm.contrib.random.random_fill_for_measure",
         "Please make sure 'USE_RANDOM' is turned ON in the config.cmake on the RPC server.",
     )
 

python/tvm/meta_schedule/testing/tune_onnx.py

@@ -71,11 +71,6 @@ def _parse_args():
         type=str,
         required=True,
     )
-    args.add_argument(
-        "--rpc-workers",
-        type=int,
-        required=True,
-    )
     args.add_argument(
         "--work-dir",
         type=str,
@@ -98,7 +93,7 @@ def _parse_args():
     )
     args.add_argument(
         "--cpu-flush",
-        type=bool,
+        type=int,
         required=True,
     )
     parsed = args.parse_args()
@@ -140,7 +135,6 @@ def main():
             enable_cpu_cache_flush=ARGS.cpu_flush,
         ),
         alloc_repeat=1,
-        max_workers=ARGS.rpc_workers,
     )
     with ms.Profiler() as profiler:
         lib = ms.tune_relay(