Backport Fix preserve_rng_state for activation checkpointing (#4690) (#…

…6788)

Co-authored-by: Fei <[email protected]>

test/test_operations.py

@@ -32,6 +32,7 @@
 import torch_xla.core.xla_builder as xb
 import torch_xla.core.xla_op_registry as xor
 import torch_xla.distributed.data_parallel as dp
+from torch_xla.distributed.fsdp import checkpoint_module
 from torch_xla.distributed.fsdp.utils import apply_xla_patch_to_nn_linear
 import torch_xla.debug.metrics as met
 import torch_xla.debug.model_comparator as mc
@@ -2334,6 +2335,41 @@ def test_aten_move_scalar_cuda_to_xla(self):
     self._test_move_tensor_cuda_to_xla(torch.tensor(42))
 
 
+class SimpleModelWithDropout(torch.nn.Module):
+
+  def __init__(self):
+    super().__init__()
+    self.x = torch.nn.Linear(128, 128)
+    self.dropout = torch.nn.Dropout(p=0.1)
+    self.to_save = []
+
+  def save_output(self, output):
+    self.to_save.append(output.detach().cpu())
+
+  def forward(self, inp):
+    x = self.x(inp)
+    output = self.dropout(x)
+    xm.add_step_closure(self.save_output, args=(output,), run_async=False)
+    return output
+
+
+class TestActivationCheckpoint(test_utils.XlaTestCase):
+
+  def test_dropout(self):
+    device = xm.xla_device()
+    model = SimpleModelWithDropout().to(device)
+    model = checkpoint_module(model)
+    _input = torch.randn(128, 128, requires_grad=True)
+    _input = _input.to(device)
+    output = model(_input)
+    output = torch.sum(output)
+    output.backward()
+    xm.mark_step()
+    same_output = torch.allclose(model.to_save[0], model.to_save[1])
+    self.assertTrue(same_output,
+                    f"in fwd {model.to_save[0]}, in bwd {model.to_save[1]}")
+
+
 if __name__ == '__main__':
   torch.set_default_dtype(torch.float32)
   torch.manual_seed(42)

torch_xla/core/xla_model.py

@@ -1,3 +1,4 @@
+import contextlib
 import io
 import itertools
 import logging
@@ -1263,6 +1264,28 @@ def get_rng_state(device=None):
   return torch_xla._XLAC._xla_get_rng_seed(str(device) if device else '')
 
 
+@contextlib.contextmanager
+def fork_rng(device=None, enabled=True):
+  """
+  Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in.
+  Args:
+    device (string, optional): The device where the RNG state needs to be set. If missing the default device seed will be set.
+    enabled (bool): if ``False``, the RNG is not forked.  This is a convenience argument for easily disabling the context manager without having to delete it and unindent your Python code under it.
+  """
+  if not enabled:
+    yield
+    return
+
+  if device is None:
+    device = torch_xla._XLAC._xla_get_default_device()
+  xla_rng_state = get_rng_state(device=device)
+
+  try:
+    yield
+  finally:
+    set_rng_state(xla_rng_state, device=device)
+
+
 def get_memory_info(device):
   """Retrieves the device memory information.
 

torch_xla/utils/checkpoint.py

@@ -88,6 +88,7 @@ def forward(ctx, run_function, preserve_rng_state, *args):
         "cache_enabled": torch.is_autocast_cache_enabled()
     }
     if preserve_rng_state:
+      ctx.fwd_xla_state = xm.get_rng_state()
       ctx.fwd_cpu_state = torch.get_rng_state()
       # Don't eagerly initialize the cuda context by accident.
       # (If the user intends that the context is initialized later, within their
@@ -143,17 +144,21 @@ def backward(ctx, *args):
       rng_devices = ctx.fwd_gpu_devices
     xm.optimization_barrier_(
         CheckpointFunction._extract_tensors_from_list(inputs + list(args)))
+    # torch.random.fork_rng will handle the cpu and gpu seed
+    # xm.fork_rng will handle the xla device seed
     with torch.random.fork_rng(
         devices=rng_devices, enabled=ctx.preserve_rng_state):
-      if ctx.preserve_rng_state:
-        torch.set_rng_state(ctx.fwd_cpu_state)
-        if ctx.had_cuda_in_fwd:
-          set_device_states(ctx.fwd_gpu_devices, ctx.fwd_gpu_states)
-      detached_inputs = detach_variable(tuple(inputs))
-      with torch.enable_grad(), \
-           torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs), \
-           torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):
-        outputs = ctx.run_function(*detached_inputs)
+      with xm.fork_rng():
+        if ctx.preserve_rng_state:
+          xm.set_rng_state(ctx.fwd_xla_state)
+          torch.set_rng_state(ctx.fwd_cpu_state)
+          if ctx.had_cuda_in_fwd:
+            set_device_states(ctx.fwd_gpu_devices, ctx.fwd_gpu_states)
+        detached_inputs = detach_variable(tuple(inputs))
+        with torch.enable_grad(), \
+            torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs), \
+            torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):
+          outputs = ctx.run_function(*detached_inputs)
 
     if isinstance(outputs, torch.Tensor):
       outputs = (outputs,)