jwfromm commented on a change in pull request #7823:
URL: https://github.com/apache/tvm/pull/7823#discussion_r616953691
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -228,24 +240,137 @@ def drive_tune(args):
args: argparse.Namespace
Arguments from command line parser.
"""
- # extra arguments validation before importing the model, so that obvious
errors
- # are pointed in advance.
- if args.rpc_tracker:
- parsed_url = urlparse("//%s" % args.rpc_tracker)
+ tvmc_model = frontends.load_model(args.FILE, args.model_format,
shape_dict=args.input_shapes)
+ tvmc_model.tuning_records = args.tuning_records
+ # Specify hardware parameters, although they'll only be used if
autoscheduling.
+ hardware_params = auto_scheduler.HardwareParams(
+ args.num_cores,
+ args.vector_unit_bytes,
+ args.cache_line_bytes,
+ args.max_shared_memory_per_block,
+ args.max_local_memory_per_block,
+ args.max_threads_per_block,
+ args.max_vthread_extent,
+ args.warp_size,
+ args.target,
+ args.target_host,
+ )
+
+ tune_model(
+ tvmc_model,
+ args.target,
+ args.output,
+ args.enable_autoscheduler,
+ args.rpc_key,
+ args.rpc_tracker,
+ args.trials,
+ args.target_host,
+ args.tuner,
+ args.min_repeat_ms,
+ args.early_stopping,
+ args.desired_layout,
+ args.timeout,
+ args.number,
+ args.repeat,
+ args.parallel,
+ hardware_params,
+ args.include_simple_tasks,
+ args.log_estimated_latency,
+ )
+
+
+def tune_model(
+ tvmc_model: TVMCModel,
+ target: str,
+ tuning_records: Optional[str] = None,
+ enable_autoscheduler: bool = False,
+ rpc_key: Optional[str] = None,
+ rpc_tracker: Optional[str] = None,
+ trials: Optional[int] = None,
+ target_host: str = "llvm",
+ tuner: str = "xgb",
+ min_repeat_ms: Optional[int] = None,
+ early_stopping: Optional[int] = None,
+ desired_layout: Optional[str] = None,
+ timeout: int = 10,
+ number: int = 10,
+ repeat: int = 1,
+ parallel: int = 4,
+ hardware_params: Optional[HardwareParams] = None,
+ include_simple_tasks: bool = False,
+ log_estimated_latency: bool = False,
+):
+ """Use tuning to automatically optimize the functions in a model.
+
+ Parameters
+ ----------
+ tvmc_model : TVMCModel
+ The model to be optimized.
+ target : str
+ Compilation target as plain string, inline JSON or path to a JSON file.
+ tuning_records: str, optional
+ The path to a file that tuning results will be saved to. If not
specified,
+ a temporary file will be used.
Review comment:
Having `tuning_records` be optional allows very new users to not
interact with files at all. A lot of first time TVM users will just run a
simple script like this:
```
model = tvmc.load('my_model.onnx')
tuning_record = tvmc.tune(model)
package = tvmc.compile(model, tuning_record=tuning_record)
print(tvmc.run(package)
```
If you're just trying TVM out, not having to worry about where to save the
tuning_records or what extension they should have is quite nice. If you guys
don't think this specific case matters we can make the output path required.
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