Use distillation modifier from SparseML for QA task

examples/pytorch/question-answering/run_qa.py

@@ -65,12 +65,6 @@ class ModelArguments:
     distill_teacher: Optional[str] = field(
         default=None, metadata={"help": "Teacher model which needs to be a trained QA model"}
     )
-    distill_temperature: Optional[float] = field(
-        default=2.0, metadata={"help": "Temperature applied to teacher softmax for distillation."}
-    )
-    distill_hardness: Optional[float] = field(
-        default=1.0, metadata={"help": "Proportion of loss coming from teacher model."}
-    )
     config_name: Optional[str] = field(
         default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
     )
@@ -593,8 +587,6 @@ def compute_metrics(p: EvalPrediction):
         model_args.model_name_or_path,
         [existing_recipe, new_recipe],
         teacher=teacher_model,
-        distill_hardness=model_args.distill_hardness,
-        distill_temperature=model_args.distill_temperature,
         model=model,
         args=training_args,
         train_dataset=train_dataset if training_args.do_train else None,

examples/pytorch/question-answering/sparseml_utils.py

@@ -1,8 +1,6 @@
 from typing import Any
 
 import numpy
-import torch
-import torch.nn.functional as F
 
 from sparseml.pytorch.utils import ModuleExporter
 from trainer_qa import QuestionAnsweringTrainer
@@ -28,46 +26,24 @@ def compute_loss(self, model, inputs, return_outputs=False):
         if not self.recipes or self.teacher is None:
             return super().compute_loss(model, inputs, return_outputs=return_outputs)
 
-        outputs = model(**inputs)
-        if self.teacher is None:
-            loss = outputs["loss"]
-        else:
-            input_device = inputs["input_ids"].device
-            self.teacher = self.teacher.to(input_device)
-            start_logits_student = outputs["start_logits"]
-            end_logits_student = outputs["end_logits"]
-            start_logits_label = inputs["start_positions"]
-            end_logits_label = inputs["end_positions"]
-            with torch.no_grad():
-                teacher_output = self.teacher(
-                    input_ids=inputs["input_ids"],
-                    token_type_ids=inputs["token_type_ids"],
-                    attention_mask=inputs["attention_mask"],
-                )
-            start_logits_teacher = teacher_output["start_logits"]
-            end_logits_teacher = teacher_output["end_logits"]
-            loss_start = (
-                F.kl_div(
-                    input=F.log_softmax(start_logits_student / self.distill_temperature, dim=-1),
-                    target=F.softmax(start_logits_teacher / self.distill_temperature, dim=-1),
-                    reduction="batchmean",
-                )
-                * (self.distill_temperature ** 2)
-            )
-            loss_end = (
-                F.kl_div(
-                    input=F.log_softmax(end_logits_student / self.distill_temperature, dim=-1),
-                    target=F.softmax(end_logits_teacher / self.distill_temperature, dim=-1),
-                    reduction="batchmean",
-                )
-                * (self.distill_temperature ** 2)
-            )
-            teacher_loss = (loss_start + loss_end) / 2.0
-            loss_start = self.criterion(start_logits_student, start_logits_label)
-            loss_end = self.criterion(end_logits_student, end_logits_label)
-            label_loss = (loss_start + loss_end) / 2.0
-            loss = ((1 - self.distill_hardness) * label_loss) + (self.distill_hardness * teacher_loss)
-        return (loss, outputs) if return_outputs else loss
+        student_outputs = model(**inputs)
+        loss = student_outputs["loss"]
+
+        teacher_input_keys = ["input_ids", "token_type_ids", "attention_mask"]
+        teacher_inputs = {k: inputs[k] for k in teacher_input_keys}
+
+        steps_in_epoch = -1  # Unused
+        loss = self.manager.loss_update(
+            loss,
+            model,
+            self.optimizer,
+            self.state.epoch,
+            steps_in_epoch,
+            global_step=self.state.global_step,
+            student_outputs=student_outputs,
+            teacher_inputs=teacher_inputs,
+        )
+        return (loss, student_outputs) if return_outputs else loss
 
 
 class QuestionAnsweringModuleExporter(ModuleExporter):

src/transformers/sparse.py

@@ -26,15 +26,13 @@ class SparseMLTrainer(Trainer):
     :param args, kwargs: arguments passed into parent class
     """
 
-    def __init__(
-        self, model_name_or_path, recipes, teacher=None, distill_hardness=0.5, distill_temperature=2.0, *args, **kwargs
-    ):
+    def __init__(self, model_name_or_path, recipes, teacher=None, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.model_name_or_path = str(model_name_or_path)
         self.recipes = [recipe for recipe in recipes if recipe]
         self.teacher = teacher
-        self.distill_hardness = distill_hardness
-        self.distill_temperature = distill_temperature
+        if self.teacher is not None:
+            self.teacher.eval()
         self.criterion = torch.nn.CrossEntropyLoss()
 
         manager = None
@@ -57,7 +55,7 @@ def apply_recipes(self, epoch=0.0):
         """
         if self.manager is not None:
             org_state_dict = self.model.state_dict()
-            self.manager.initialize(self.model, epoch=epoch, loggers=self.loggers)
+            self.manager.initialize(self.model, epoch=epoch, distillation_teacher=self.teacher, loggers=self.loggers)
             new_state_dict = self.model.state_dict()
             new_params = [p for p in new_state_dict.keys() if p not in org_state_dict]