[tensorrt] [byoc] [plugin] Allow users to specify tunning option

python/tvm/tpat/cuda/kernel.py

@@ -23,16 +23,17 @@
 
 
 class Config(object):
-    def __init__(self, onnx_model, input_shapes, target, work_dir) -> None:
+    def __init__(self, onnx_model, input_shapes, target, tunning_option) -> None:
         self.onnx_model = onnx_model
         self.input_shapes = input_shapes
-        self.work_dir = work_dir
+        self.tunning_option = tunning_option
+        self.work_dir = tunning_option["work_dir"] or "./log_db"
 
         if target == "gpu":
             self.target = self._detect_cuda_target()
 
     def tune_option(self):
-        return {
+        default = {
             "target": self.target,
             "builder": ms.builder.LocalBuilder(),
             "runner": ms.runner.LocalRunner(),
@@ -41,6 +42,9 @@ def tune_option(self):
             "work_dir": self.work_dir,
         }
 
+        default.update(self.tunning_option)
+        return default
+
     def _detect_cuda_target(self):
         dev = tvm.cuda()
         if not dev.exist:
@@ -59,10 +63,10 @@ def _detect_cuda_target(self):
 
 
 class Kernel(object):
-    def __init__(self, name, onnx_model, input_shapes, enable_tunning, work_dir):
+    def __init__(self, name, onnx_model, input_shapes, enable_tunning, tunning_option):
         self._name = name
         self._enable_tunning = enable_tunning
-        self._config = Config(onnx_model, input_shapes, "gpu", work_dir)
+        self._config = Config(onnx_model, input_shapes, "gpu", tunning_option)
 
         self._lib = None
         self._module = None

python/tvm/tpat/cuda/pipeline.py

@@ -59,7 +59,7 @@ def _extract_target_onnx_node(model, tunning_node):
 
 
 def pipeline(
-    onnx_file: str, node_names: list[str], enable_tunning: bool, work_dir: str, output_onnx: str
+    onnx_file: str, node_names: list[str], enable_tunning: bool, tunning_option: object, output_onnx: str
 ) -> Tuple[str, list[str]]:
     """Generate plugins for specified nodes in an ONNX model.
 
@@ -73,8 +73,8 @@ def pipeline(
         Names of the nodes to be generated as TensorRT plugins.
     enable_tunning : bool
         Flag indicating whether tunning is enabled.
-    work_dir : str
-        Path to the tunning log file where the records will be saved.
+    tunning_option : object
+        Tunning option provided for ms.relay_integration.tune_relay, you don't need to specify mod, params and target.
     output_onnx : str
         Path to the output ONNX file where the modified model will be saved.
 
@@ -106,7 +106,7 @@ def pipeline(
 
         subgraph, submodel, shapes = _extract_target_onnx_node(inferred_model, node)
 
-        kernel = Kernel(plugin_name, submodel, shapes, enable_tunning, work_dir)
+        kernel = Kernel(plugin_name, submodel, shapes, enable_tunning, tunning_option)
         kernel.run()
 
         ## 3.1 fill in template

python/tvm/tpat/cuda/plugin/Makefile

@@ -14,9 +14,12 @@
 # limitations under the License.
 #
 
+# Variables need to be defined by Users
 CUDA_PATH   = /path/to/cuda
 CUDNN_PATH = /path/to/cudnn
 TRT_PATH = /path/to/TensorRT
+ARCH = sm_86
+########################################
 
 CUDA_INC_PATH = $(CUDA_PATH)/include
 CUDA_LIB_PATH  = $(CUDA_PATH)/lib
@@ -28,13 +31,9 @@ CUDNN_LIB_PATH = $(CUDNN_PATH)/lib
 TRT_INC_PATH   = $(TRT_PATH)/include
 TRT_LIB_PATH = $(TRT_PATH)/lib
 
-
-ARCH = sm_86
 GCC = g++
 NVCC = $(CUDA_PATH)/bin/nvcc
-# CCFLAGS = -g -std=c++11 -DNDEBUG
 CCFLAGS = -w -std=c++11
-# CCFLAGS+= -DDEBUG_ME
 INCLUDES := -I. -I$(CUDA_COM_PATH) -I$(CUDA_INC_PATH) -I$(CUDNN_INC_PATH) -I$(TRT_INC_PATH) -I/usr/include
 
 LDFLAGS := -L$(CUDA_LIB_PATH) -L$(CUDNN_LIB_PATH) -L$(TRT_LIB_PATH)

tests/python/tpat/cuda/common.py

@@ -94,7 +94,11 @@ def name_without_num(name):
     ops_name = [op_name]
 
     _, trt_plugin_names = tpat.cuda.pipeline(
-        INPUT_MODEL_FILE, ops_name, False, "./log_db", OUTPUT_MODEL_FILE
+        INPUT_MODEL_FILE,
+        ops_name,
+        False,
+        {"work_dir": "./log_db", "max_trials_per_task": 500},
+        OUTPUT_MODEL_FILE,
     )
 
     load_plugin(trt_plugin_names)
@@ -197,7 +201,11 @@ def verify_with_ort_with_trt(
     ops_name = [op_name]
 
     _, trt_plugin_names = tpat.cuda.pipeline(
-        INPUT_MODEL_FILE, ops_name, False, "./log_db", OUTPUT_MODEL_FILE
+        INPUT_MODEL_FILE,
+        ops_name,
+        False,
+        {"work_dir": "./log_db", "max_trials_per_task": 500},
+        OUTPUT_MODEL_FILE,
     )
 
     load_plugin(trt_plugin_names)