Hard ban on fp16 BCELoss

apex/amp/amp.py

@@ -54,7 +54,7 @@ def register_promote_function(module, name):
     _USER_PROMOTE_REGISTRY.add((module, name))
 
 # Top-level function to insert _all_ the hooks.
-def init(enabled=True, enable_caching=True, verbose=False):
+def init(enabled=True, enable_caching=True, verbose=False, allow_banned=False):
     global _DECORATOR_HANDLE
 
     if not enabled:
@@ -145,5 +145,10 @@ def init(enabled=True, enable_caching=True, verbose=False):
     # 5.5) Extra-special handling of RNN backend
     wrap.rnn_cast(torch.nn.backends.thnn.backend, 'RNN', verbose)
 
+    # 6) Place error+print message on banned functions
+    if not allow_banned:
+        for fn, err_msg in functional_overrides.BANNED_FUNCS:
+            wrap.err_if_any_half(functional_overrides.MODULE, fn, err_msg)
+
     _DECORATOR_HANDLE = handle
     return handle

apex/amp/lists/functional_overrides.py

@@ -42,7 +42,6 @@ FP32_FUNCS = [
 
     # Loss functions
     # TODO: which of these can be fp16?
-    'binary_cross_entropy',
     'poisson_nll_loss',
     'cosine_embedding_loss',
     'cross_entropy',
@@ -60,3 +59,15 @@ FP32_FUNCS = [
     'soft_margin_loss',
     'triplet_margin_loss'
 ]
+
+BANNED_FUNCS = [
+    ('binary_cross_entropy',
+     ("\namp does not work out-of-the-box with `F.binary_cross_entropy` or `torch.nn.BCELoss.` "
+      "It requires that the output of the previous function be already a FloatTensor. \n\n"
+      "Most models have a Sigmoid right before BCELoss. In that case, you can use\n"
+      "    torch.nn.BCEWithLogitsLoss\nto combine Sigmoid+BCELoss into a single layer "
+      "that is compatible with amp.\nAnother option is to add\n"
+      "    amp.register_float_function(torch, 'sigmoid')\nbefore calling `amp.init()`.\n"
+      "If you _really_ know what you are doing, you can disable this warning by passing "
+      "allow_banned=True to `amp.init()`."))
+]

apex/amp/wrap.py

@@ -94,7 +94,7 @@ def promote_match_arg0(mod, fn, verbose=False):
         return orig_fn(arg0, *new_args, **kwargs)
     utils.set_func(mod, fn, wrapper)
 
-def err_if_any_half(mod, fn):
+def err_if_any_half(mod, fn, custom_err_msg=None):
     if not utils.has_func(mod, fn):
         return
 
@@ -103,6 +103,9 @@ def err_if_any_half(mod, fn):
     def wrapper(*args, **kwargs):
         types = utils.collect_fp_tensor_types(args, kwargs)
         if 'HalfTensor' in types:
+            if custom_err_msg:
+                raise NotImplementedError(custom_err_msg)
+            else:
                 raise NotImplementedError('Cannot call in-place function ' +
                                           '{} with fp16 arguments.'.format(fn))
         else: