X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=ffutils.py;fp=ffutils.py;h=45f44d86cbd09c29e6888a081df5fb100a182414;hb=0f580d4facb4b4b485d0a38d62d06c0639715b77;hp=0000000000000000000000000000000000000000;hpb=3dea181a5903a0e577e4830c66405b40f2a2df1d;p=picoclvr.git

diff --git a/ffutils.py b/ffutils.py
new file mode 100755
index 0000000..45f44d8
--- /dev/null
+++ b/ffutils.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import torch
+import sys, contextlib
+
+import torch
+from torch import Tensor
+
+######################################################################
+
+
+@contextlib.contextmanager
+def evaluation(*models):
+    with torch.inference_mode():
+        t = [(m, m.training) for m in models]
+        for m in models:
+            m.train(False)
+        yield
+        for m, u in t:
+            m.train(u)
+
+
+######################################################################
+
+from torch.utils._python_dispatch import TorchDispatchMode
+
+
+def hasNaN(x):
+    if torch.is_tensor(x):
+        return x.isnan().max()
+    else:
+        try:
+            return any([hasNaN(y) for y in x])
+        except TypeError:
+            return False
+
+
+class NaNDetect(TorchDispatchMode):
+    def __torch_dispatch__(self, func, types, args, kwargs=None):
+        kwargs = kwargs or {}
+        res = func(*args, **kwargs)
+
+        if hasNaN(res):
+            raise RuntimeError(
+                f"Function {func}(*{args}, **{kwargs}) " "returned a NaN"
+            )
+        return res
+
+
+######################################################################
+
+
+def exception_hook(exc_type, exc_value, tb):
+    r"""Hacks the call stack message to show all the local variables
+    in case of relevant error, and prints tensors as shape, dtype and
+    device.
+
+    """
+
+    repr_orig = Tensor.__repr__
+    Tensor.__repr__ = lambda x: f"{x.size()}:{x.dtype}:{x.device}"
+
+    while tb:
+        print("--------------------------------------------------\n")
+        filename = tb.tb_frame.f_code.co_filename
+        name = tb.tb_frame.f_code.co_name
+        line_no = tb.tb_lineno
+        print(f'  File "{filename}", line {line_no}, in {name}')
+        print(open(filename, "r").readlines()[line_no - 1])
+
+        if exc_type in {RuntimeError, ValueError, IndexError, TypeError}:
+            for n, v in tb.tb_frame.f_locals.items():
+                print(f"  {n} -> {v}")
+
+        print()
+        tb = tb.tb_next
+
+    Tensor.__repr__ = repr_orig
+
+    print(f"{exc_type.__name__}: {exc_value}")
+
+
+def activate_tensorstack():
+    sys.excepthook = exception_hook
+
+
+######################################################################
+
+if __name__ == "__main__":
+    import torch
+
+    def dummy(a, b):
+        print(a @ b)
+
+    def blah(a, b):
+        c = b + b
+        dummy(a, c)
+
+    mmm = torch.randn(2, 3)
+    xxx = torch.randn(3)
+    # print(xxx@mmm)
+    blah(mmm, xxx)
+    blah(xxx, mmm)