Revert D22801628.

pytext/config/pytext_config.py

@@ -111,17 +111,6 @@ class PyTextConfig(ConfigBase):
     export_torchscript_path: Optional[str] = None
     # Export quantized torchscript model
     torchscript_quantize: Optional[bool] = False
-    # Accelerator options.
-    # Options:
-    # "half" - demote model to half precision
-    # "nnpi" - freeze model for use with Glow on NNPI accelerator
-    accelerate: List[str] = []
-    # Inference Interface.
-    # Specifies which of the 3 optional list parameters a model takes,
-    # when the model implements the inference_ionterface() method.:
-    # Possible values: texts, multi_texts, tokens (and/or others as
-    # supported by inference_interface method).
-    inference_interface: Optional[str] = None
     # Base directory where modules are saved
     modules_save_dir: str = ""
     # Whether to save intermediate checkpoints for modules if they are best yet

pytext/task/new_task.py

@@ -279,14 +279,7 @@ def export(self, model, export_path, metric_channels=None, export_onnx_path=None
         )
 
     def torchscript_export(
-        self,
-        model,
-        export_path=None,
-        quantize=False,
-        sort_input=False,
-        sort_key=1,
-        inference_interface=None,
-        accelerate=[],  # noqa mutable default is read only
+        self, model, export_path=None, quantize=False, sort_input=False, sort_key=1
     ):
         # Make sure to put the model on CPU and disable CUDA before exporting to
         # ONNX to disable any data_parallel pieces
@@ -310,13 +303,7 @@ def torchscript_export(
         model(*inputs)
         if quantize:
             model.quantize()
-        if "half" in accelerate:
-            model.half()
-        if inference_interface is not None:
-            model.inference_interface(inference_interface)
-        trace = jit.trace(model, inputs)
-        if "nnpi" in accelerate:
-            trace._c = torch._C._freeze_module(trace._c)
+        trace = model.trace(inputs)
         if hasattr(model, "torchscriptify"):
             trace = model.torchscriptify(self.data.tensorizers, trace)
         trace.apply(lambda s: s._pack() if s._c._has_method("_pack") else None)

pytext/workflow.py

@@ -197,8 +197,6 @@ def save_and_export(
             model=task.model,
             export_path=config.export_torchscript_path,
             quantize=config.torchscript_quantize,
-            inference_interface=config.inference_interface,
-            accelerate=config.accelerate,
         )
 
 
@@ -219,14 +217,10 @@ def export_saved_model_to_caffe2(
 
 
 def export_saved_model_to_torchscript(
-    saved_model_path: str,
-    path: str,
-    quantize: bool = False,
-    inference_interface: Optional[str] = None,
-    accelerate: List[str] = [],  # noqa mutable default is read only
+    saved_model_path: str, path: str, quantize: bool = False
 ) -> None:
     task, train_config, _training_state = load(saved_model_path)
-    task.torchscript_export(task.model, path, quantize, inference_interface, accelerate)
+    task.torchscript_export(task.model, path, quantize)
 
 
 def test_model(