Automatic torch.autocast for IPEXModel (#542)

* Handle autocast in IPEXModel.forward

* Handle missing torch_dtype in config

Author: ofirzaf

optimum/intel/ipex/modeling_base.py

@@ -69,6 +69,7 @@ def __init__(
         OptimizedModel.__init__(self, model=model, config=config)
         # To do: add XPU support
         self._device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self._dtype = self.config.torch_dtype if self.config.torch_dtype is not None else torch.float32
         self.model.to(self._device)
         self.model_save_dir = model_save_dir
 
@@ -188,7 +189,7 @@ def forward(
         if "token_type_ids" in self.input_names:
             inputs["token_type_ids"] = token_type_ids
 
-        outputs = self.model(**inputs)
+        outputs = self._call_model(**inputs)
         return ModelOutput(**outputs) if isinstance(outputs, dict) else ModelOutput(logits=outputs[0])
 
     def eval(self):
@@ -199,6 +200,10 @@ def eval(self):
     def device(self) -> torch.device:
         return self._device
 
+    @property
+    def dtype(self) -> torch.dtype:
+        return self._dtype
+
     def to(self, device: Union[torch.device, str]):
         self._device = device if isinstance(device, torch.device) else torch.device(device)
         self.model.to(self._device)
@@ -207,6 +212,14 @@ def to(self, device: Union[torch.device, str]):
     def can_generate(self):
         return isinstance(self, GenerationMixin)
 
+    def _call_model(self, *args, **kwargs):
+        try:
+            with torch.autocast(self.device.type, self.dtype):
+                out = self.model(*args, **kwargs)
+        except RuntimeError:
+            out = self.model(*args, **kwargs)
+        return out
+
 
 class IPEXModelForSequenceClassification(IPEXModel):
     auto_model_class = AutoModelForSequenceClassification
@@ -236,7 +249,7 @@ def forward(
             "pixel_values": pixel_values,
         }
 
-        outputs = self.model(**inputs)
+        outputs = self._call_model(**inputs)
         return ModelOutput(**outputs) if isinstance(outputs, dict) else ModelOutput(logits=outputs[0])
 
 
@@ -257,7 +270,7 @@ def forward(
         if "attention_mask" in self.input_names:
             inputs["attention_mask"] = attention_mask
 
-        outputs = self.model(**inputs)
+        outputs = self._call_model(**inputs)
         return ModelOutput(**outputs) if isinstance(outputs, dict) else ModelOutput(logits=outputs[0])
 
 
@@ -266,7 +279,7 @@ class IPEXModelForQuestionAnswering(IPEXModel):
     export_feature = "question-answering"
 
     def forward(self, *args, **kwargs):
-        outputs = self.model(*args, **kwargs)
+        outputs = self._call_model(*args, **kwargs)
         start_logits = outputs["start_logits"] if isinstance(outputs, dict) else outputs[0]
         end_logits = outputs["end_logits"] if isinstance(outputs, dict) else outputs[1]
         return ModelOutput(start_logits=start_logits, end_logits=end_logits)
@@ -287,7 +300,7 @@ def __init__(
         super().__init__(model, config, model_save_dir=model_save_dir)
 
         self.normalized_config = NormalizedConfigManager.get_normalized_config_class(config.model_type)(config)
-        self.model_dtype = kwargs.get("model_dtype", None)
+        self.model_dtype = kwargs.get("model_dtype", self.dtype)
         self.use_cache = "past_key_values" in self.input_names
 
         if use_cache ^ self.use_cache:
@@ -377,7 +390,7 @@ def forward(
             inputs["past_key_values"] = past_key_values
 
         # 2. Model forward
-        outputs = self.model(**inputs)
+        outputs = self._call_model(**inputs)
 
         # 3. Process model outputs
         if isinstance(outputs, (list, tuple)):