Upcast FSDP2 parameters only if requires_grad

src/accelerate/utils/fsdp_utils.py

@@ -707,13 +707,17 @@ def fsdp2_prepare_model(accelerator, model: torch.nn.Module) -> torch.nn.Module:
     # Set it to None if it doesn't exist and do the upcast always
     model_dtype = getattr(model, "dtype", None)
     if accelerator.mixed_precision != "no" and (model_dtype is None or model_dtype != torch.float32):
-        # We upcast the model according to `deepspeed`'s implementation
+        # We upcast the trainable parameters according to `deepspeed`'s implementation
         # More info about this can be found in `accelerator.py:prepare_model`s FSDP1 section
-        model = model.to(torch.float32)
-        if accelerator.is_main_process:
-            # TODO(siro1): Add a warning for each parameter that was upcasted
+        upcasted_params = []
+        for name, param in model.named_parameters():
+            if param.requires_grad and param.dtype != torch.float32:
+                upcasted_params.append(name)
+                param.data = param.data.to(torch.float32)
+        if accelerator.is_main_process and upcasted_params:
             warnings.warn(
-                "FSDP upcast of low precision parameters to fp32 (since mixed_precision != 'no') may affect the precision of model checkpoints."
+                "FSDP upcast of low precision parameters to fp32 (since mixed_precision != 'no') may affect the precision of model checkpoints. "
+                f"This effects {len(upcasted_params)} parameters: {upcasted_params}..."
             )
     return model