diff --git a/torch_xla/experimental/spmd_fully_sharded_data_parallel.py b/torch_xla/experimental/spmd_fully_sharded_data_parallel.py
index b1f5d709920..dc37ff7d9c7 100644
--- a/torch_xla/experimental/spmd_fully_sharded_data_parallel.py
+++ b/torch_xla/experimental/spmd_fully_sharded_data_parallel.py
@@ -1,17 +1,21 @@
-from typing import (Any, Callable, Dict, Optional, Union)
 import warnings
+from collections import OrderedDict
+from typing import (Any, Callable, Dict, Generator, List, Optional, Set, Tuple,
+                    Union, cast)
 
+import numpy as np
 import torch
 import torch.nn as nn
 from torch._prims_common import TensorLike, TensorSequenceType
-
-import numpy as np
+from torch.nn.utils.rnn import PackedSequence
 
 import torch_xla
 import torch_xla.core.xla_model as xm
 import torch_xla.distributed.spmd as spmd
-from torch_xla.distributed.fsdp.wrap import recursive_wrap
 from torch_xla.distributed.fsdp._init_utils import _materialize_module
+from torch_xla.distributed.fsdp.wrap import recursive_wrap
+
+FLOAT_DTYPES = [torch.float32, torch.float16, torch.bfloat16]
 
 
 def _prepare_spmd_partition_spec(param):
@@ -40,6 +44,10 @@ class SpmdFullyShardedDataParallel(nn.Module):
       The callable should have the signature (output, mesh) -> None.
       If None, the default implementation will shard the first tensor in the output.
       If the output is a tuple, only the first tensor will be sharded.
+    compute_dtype (torch.dtype, Optional):
+      dtype for full parameters for computation. This defaults to
+      ``torch.float32`` but can be set to ``torch.float16`` or
+      ``torch.bfloat16``. The sharded parameters will always be in FP32.
   """
 
   def __init__(
@@ -47,6 +55,7 @@ def __init__(
       module: nn.Module,
       mesh: Optional[spmd.Mesh] = None,
       shard_output: Optional[Callable] = None,
+      compute_dtype: Optional[torch.dtype] = None,
       auto_wrap_policy: Optional[Callable] = None,
       auto_wrapper_callable: Optional[Callable] = None,
   ):
@@ -96,6 +105,11 @@ def __init__(
       )
       self._auto_wrap(auto_wrap_kwargs, fsdp_kwargs)
 
+    if compute_dtype is not None and compute_dtype not in FLOAT_DTYPES:
+      raise ValueError(
+          f"compute_dtype must be one of {FLOAT_DTYPES}, not {compute_dtype}")
+    self.compute_dtype = compute_dtype or torch.float32
+
     _materialize_module(
         module,
         None, [],
@@ -150,6 +164,9 @@ def module(self) -> nn.Module:
     return self._orig_module
 
   def forward(self, *args: Any, **kwargs: Any) -> torch.Tensor:
+    if self.compute_dtype != torch.float32:
+      # Cast the input float tensors to the specified compute_dtype
+      args, kwargs = _cast_floats_tensors(self.compute_dtype, *args, **kwargs)
     output = self.module(*args, **kwargs)
     # Need to shard the output of the forward to instruct the compiler
     # to enforce the FSDP algorithm.
@@ -192,3 +209,49 @@ def _auto_wrap(
             "if using an `auto_wrap_policy`")
 
     recursive_wrap(**auto_wrap_kwargs, **fsdp_kwargs)
+
+
+def _cast_floats_tensors(dtype: torch.dtype, *args: Any,
+                         **kwargs: Any) -> Tuple[Any, Any]:
+  """
+  Cast floating point Tensors in *args or **kwargs to dtype if they are not.
+  """
+
+  def fn(t):
+    if t.dtype != dtype and torch.is_floating_point(t):
+      t = t.to(dtype)
+    return t
+
+  return apply_to_tensors(fn, args), apply_to_tensors(fn, kwargs)
+
+
+def apply_to_tensors(
+    fn: Callable, container: Union[torch.Tensor, Dict, List, Tuple, Set]
+) -> Union[torch.Tensor, Dict, List, Tuple, Set]:
+  """Recursively apply to all tensor in different kinds of container types."""
+
+  def _apply(
+      x: Union[torch.Tensor, Dict, List, Tuple, Set]
+  ) -> Union[torch.Tensor, Dict, List, Tuple, Set]:
+    if torch.is_tensor(x):
+      return fn(x)
+    elif isinstance(x, OrderedDict):
+      od = x.__class__()
+      for key, value in x.items():
+        od[key] = _apply(value)
+      return od
+    elif isinstance(x, PackedSequence):
+      _apply(x)
+      return x
+    elif isinstance(x, dict):
+      return {key: _apply(value) for key, value in x.items()}
+    elif isinstance(x, list):
+      return [_apply(x) for x in x]
+    elif isinstance(x, tuple):
+      return tuple(_apply(x) for x in x)
+    elif isinstance(x, set):
+      return {_apply(x) for x in x}
+    else:
+      return x
+
+  return _apply(container)