[TVM][AutoTVM] cast filepath arguments to string (apache#3968)

python/tvm/autotvm/task/dispatcher.py

@@ -41,6 +41,7 @@
 
 logger = logging.getLogger('autotvm')
 
+
 class DispatchContext(object):
     """
     Base class of dispatch context.
@@ -281,8 +282,12 @@ def load(self, records):
                        Each row of this file is an encoded record pair.
             Otherwise, it is an iterator.
         """
+        from pathlib import Path
         from ..record import load_from_file
 
+        if isinstance(records, Path):
+            records = str(records)
+
         if isinstance(records, str):
             records = load_from_file(records)
         if not records:
@@ -404,8 +409,10 @@ def update(self, target, workload, cfg):
         key = (str(target), workload)
         self.memory[key] = cfg
 
+
 DispatchContext.current = FallbackContext()
 
+
 def clear_fallback_cache(target, workload):
     """Clear fallback cache. Pass the same argument as _query_inside to this function
     to clean the cache.
@@ -426,6 +433,7 @@ def clear_fallback_cache(target, workload):
         context = context._old_ctx
     context.clear_cache(target, workload)
 
+
 class ApplyGraphBest(DispatchContext):
     """Load the graph level tuning optimal schedules.
 

python/tvm/autotvm/tuner/callback.py

@@ -26,6 +26,7 @@
 
 logger = logging.getLogger('autotvm')
 
+
 def log_to_file(file_out, protocol='json'):
     """Log the tuning records into file.
     The rows of the log are stored in the format of autotvm.record.encode.
@@ -51,6 +52,11 @@ def _callback(_, inputs, results):
         else:
             for inp, result in zip(inputs, results):
                 file_out.write(record.encode(inp, result, protocol) + "\n")
+
+    from pathlib import Path
+    if isinstance(file_out, Path):
+        file_out = str(file_out)
+
     return _callback
 
 

python/tvm/module.py

@@ -112,18 +112,9 @@ def export_library(self,
         kwargs : dict, optional
             Additional arguments passed to fcompile
         """
-        if self.is_empty():
-            logging.info("The lib generated by the NNVM compiler does not contain optimized "
-                         "functions for any operators. This usually happens when an external "
-                         "accelerator, e.g. TensorRT, is employed along with TVM to compile "
-                         "the model, and all the operators in the model are supported by the "
-                         "external accelerator at runtime. Therefore, "
-                         "the NNVM compiler skipped optimizing them at the compile time.")
-            if os.path.isfile(file_name):
-                logging.warning("Lib file %s exists, and will be overwritten by the newly created"
-                                " lib with the same name.", file_name)
-            open(file_name, 'w').close()
-            return
+        from pathlib import Path
+        if isinstance(file_name, Path):
+            file_name = str(file_name)
 
         if self.type_key == "stackvm":
             if not file_name.endswith(".stackvm"):