Fix fp16 by converting outputs back to FP32 (#134)

* Fix FP16 mode by converting model outputs to FP32

* Add context manager and doc

* Update src/accelerate/accelerator.py

Co-authored-by: Lysandre Debut <[email protected]>

Co-authored-by: Lysandre Debut <[email protected]>

docs/source/quicktour.rst

@@ -345,6 +345,19 @@ If you are using gradient clipping in your script, you should replace the calls
 :obj:`torch.nn.utils.clip_grad_norm_` or :obj:`torch.nn.utils.clip_grad_value_` with :obj:`accelerator.clip_grad_norm_`
 and :obj:`accelerator.clip_grad_value_` respectively.
 
+Mixed Precision training
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you are running your training in Mixed Precision with Accelerate, you will get the best result with your loss being
+computed inside your model (like in Transformer models for instance). Every computation outside of the model will be
+executed in full precision (which is generally what you want for loss computation, expecially if it involves a
+softmax). However you might want to put your loss computation inside the `accelerator.autocast` context manager:
+
+.. codeblock::
+
+    with accelerator.autocast():
+        loss = complex_loss_function(outputs, target):
+
 
 Internal mechanism
 -----------------------------------------------------------------------------------------------------------------------

src/accelerate/accelerator.py

@@ -28,6 +28,7 @@
 from .utils import (
     DeepSpeedPlugin,
     RNGType,
+    convert_outputs_to_fp32,
     extract_model_from_parallel,
     gather,
     pad_across_processes,
@@ -295,6 +296,7 @@ def prepare_model(self, model):
             model = torch.nn.parallel.DistributedDataParallel(model, **kwargs)
         if self.native_amp:
             model.forward = torch.cuda.amp.autocast()(model.forward)
+            model.forward = convert_outputs_to_fp32(model.forward)
         return model
 
     def _prepare_deepspeed(self, *args):
@@ -550,3 +552,17 @@ def get_state_dict(self, model):
                 state_dict[k] = state_dict[k].float()
 
         return state_dict
+
+    @contextmanager
+    def autocast(self):
+        """
+        Will apply automatic mixed-precision inside the block inside this context manager, if it is enabled. Nothing
+        different will happen otherwise.
+        """
+        if self.native_amp:
+            autocast_context = torch.cuda.amp.autocast()
+            autocast_context.__enter__()
+            yield
+            autocast_context.__exit__()
+        else:
+            yield

src/accelerate/utils.py

@@ -137,6 +137,46 @@ def send_to_device(tensor, device):
     return tensor.to(device)
 
 
+def convert_to_fp32(tensor):
+    """
+    Recursively converts the lements nested list/tuple/dictionary of tensors in FP16 precision to FP32.
+
+    Args:
+        tensor (nested list/tuple/dictionary of :obj:`torch.Tensor`):
+            The data to convert from FP16 to FP32.
+
+    Returns:
+        The same data structure as :obj:`tensor` with all tensors that were in FP16 precision converted to FP32.
+    """
+    if isinstance(tensor, (list, tuple)):
+        return honor_type(tensor, (convert_to_fp32(t) for t in tensor))
+    elif isinstance(tensor, dict):
+        return type(tensor)({k: convert_to_fp32(v) for k, v in tensor.items()})
+    elif not hasattr(tensor, "dtype") or tensor.dtype != torch.float16:
+        return tensor
+    return tensor.float()
+
+
+def convert_outputs_to_fp32(model_forward):
+    """
+    Decorator to apply to a function outputing tensors (like a model forward pass) that ensures the outputs in FP16
+    precision will be convert back to FP32.
+
+    Args:
+        model_forward (:obj:`Callable`):
+            The function which outputs we want to treat.
+
+    Returns:
+        The same function as :obj:`model_forward` but with converted outputs.
+    """
+
+    def convert_outputs(*args, **kwargs):
+        outputs = model_forward(*args, **kwargs)
+        return convert_to_fp32(outputs)
+
+    return convert_outputs
+
+
 def extract_model_from_parallel(model):
     """
     Extract a model from its distributed containers.