add async nan check utils (#965)

Summary: Pull Request resolved: #965

Reviewed By: galrotem

Differential Revision: D68530393

fbshipit-source-id: 8afdee2dc74a28b19c0a16eebf0b585f333fdc12

tests/utils/test_nan.py

@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+import unittest
+
+import torch
+
+from torchtnt.utils.nan import check_for_nan_or_inf, register_nan_hooks_on_whole_graph
+
+
+class NaNFunction(torch.autograd.Function):
+    @staticmethod
+    # pyre-ignore overrides method defined in `torch.autograd.function._SingleLevelFunction` inconsistently
+    def forward(ctx, input):
+        return input.clone()
+
+    @staticmethod
+    # pyre-ignore overrides method defined in `torch.autograd.function._SingleLevelFunction` inconsistently
+    def backward(ctx, grad_output):
+        return torch.tensor([float("nan")], device="cpu")
+
+
+class NanHookTest(unittest.TestCase):
+    def test_register_nan_hooks_on_whole_graph(self) -> None:
+        x = torch.tensor([1.0], device="cpu", requires_grad=True)
+        out = NaNFunction.apply(x)
+
+        # no error is thrown
+        out.backward()
+
+        _ = register_nan_hooks_on_whole_graph([out])
+        with self.assertRaisesRegex(RuntimeError, "Detected NaN"):
+            out.backward()
+
+    def test_check_for_nan_or_inf(self) -> None:
+        tensor = torch.tensor([float("nan")], device="cpu")
+
+        with self.assertRaisesRegex(RuntimeError, "Detected NaN or Inf in tensor"):
+            check_for_nan_or_inf(tensor)
+
+        tensor = torch.tensor([float("inf")], device="cpu")
+        with self.assertRaisesRegex(RuntimeError, "Detected NaN or Inf in tensor"):
+            check_for_nan_or_inf(tensor)

torchtnt/utils/__init__.py

@@ -51,6 +51,7 @@
     ModuleSummary,
     prune_module_summary,
 )
+from .nan import check_for_nan_or_inf, register_nan_hooks_on_whole_graph
 from .oom import (
     attach_oom_observer,
     is_out_of_cpu_memory,
@@ -89,6 +90,8 @@
 )
 
 __all__ = [
+    "check_for_nan_or_inf",
+    "register_nan_hooks_on_whole_graph",
     "IsNaNEvaluator",
     "ThresholdEvaluator",
     "CheckpointPath",

torchtnt/utils/nan.py

@@ -0,0 +1,107 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections import deque
+from typing import Callable, Iterator, List, Optional, Sequence, Union
+
+import torch
+from pyre_extensions import none_throws
+from torch.autograd.graph import GradientEdge, Node
+from torch.utils.hooks import RemovableHandle
+
+
+def _get_grad_fn_or_grad_acc(t: Union[torch.Tensor, GradientEdge]) -> Node:
+    if isinstance(t, torch.Tensor):
+        return none_throws(t.grad_fn)
+    else:
+        # pyre-ignore Undefined attribute [16]: `GradientEdge` has no attribute `function`.
+        return t.function if t is not None else None
+
+
+def register_nan_hooks_on_whole_graph(  # noqa: C901
+    t_outputs: Sequence[Union[torch.Tensor, GradientEdge]]
+) -> Callable[[], None]:
+    """
+    Registers a nan hook on the whole graph of the given tensors. The hook will throw error if a nan is detected.
+
+    This is useful if you want training to halt when a nan is detected during autograd process (ie loss is inf or nan).
+
+    Usage:
+
+        >>> class NaNFunction(torch.autograd.Function):
+                @staticmethod
+                def forward(ctx, input):
+                    return input.clone()
+
+                @staticmethod
+                def backward(ctx, grad_output):
+                    return torch.tensor([float("nan")], device="cpu")
+        >>> x = torch.tensor([1.0], device="cpu", requires_grad=True)
+        >>> out = NaNFunction.apply(x)
+        >>> _ = register_nan_hooks_on_whole_graph([out])
+        >>> out.backward()
+        RuntimeError: Detected NaN in 'grad_inputs[0]' after executing Node
+
+    """
+
+    grad_fns = list(map(_get_grad_fn_or_grad_acc, t_outputs))
+
+    def iter_graph(roots: List[torch.autograd.graph.Node]) -> Iterator[Node]:
+        if not roots:
+            return
+        seen = set()
+        q = deque()
+        for node in roots:
+            if node is not None and node not in seen:
+                seen.add(node)
+                q.append(node)
+        while q:
+            node = q.popleft()
+            for fn, _ in node.next_functions:
+                if fn is None or fn in seen:
+                    continue
+                seen.add(fn)
+                q.append(fn)
+            yield node
+
+    def _assert_no_nan_tensor(t: Optional[torch.Tensor], msg: str) -> None:
+        if t is not None:
+            torch._assert_async(torch.logical_not(torch.any(torch.isnan(t))), msg)
+
+    def posthook(
+        grad_inputs: Sequence[Optional[torch.Tensor]],
+        grad_outputs: Sequence[Optional[torch.Tensor]],
+    ) -> None:
+        node = torch._C._current_autograd_node()
+        for i, g_in in enumerate(grad_inputs):
+            _assert_no_nan_tensor(
+                g_in, f"Detected NaN in 'grad_inputs[{i}]' after executing Node: {node}"
+            )
+
+    handles: List[RemovableHandle] = []
+    for node in iter_graph(grad_fns):
+        posthandle = node.register_hook(posthook)
+        handles.append(posthandle)
+
+    def unregister_hooks() -> None:
+        for handle in handles:
+            handle.remove()
+
+    return unregister_hooks
+
+
+def check_for_nan_or_inf(
+    tensor: torch.Tensor, msg: str = "Detected NaN or Inf in tensor"
+) -> None:
+    """
+    Asynchronously assert that the tensor is neither NaN nor infinity. This will
+    produce a cuda device side assert error if tensor on gpu.
+    """
+
+    torch._assert_async(
+        torch.logical_not(torch.any(torch.isnan(tensor) | torch.isinf(tensor))),
+        msg,
+    )