Conditionally run bmm functions in fp16 based on cuda version

apex/amp/lists/torch_overrides.py

 import torch
 
+from .. import utils
+
 MODULE = torch
 
 FP16_FUNCS = [
@@ -20,10 +22,8 @@ FP16_FUNCS = [
     'matmul',
     'mm',
     'mv',
-
 ]
 
-# TODO: ban in-place versions of these in fp16
 FP32_FUNCS = [
     # Pointwise
     'acos',
@@ -54,15 +54,21 @@ FP32_FUNCS = [
     'sum',
     'var',
 
-    # Special reduction-like BLAS
-    'addbmm',
-    'baddbmm',
-    'bmm',
-
     # Misc
     'renorm'
 ]
 
+# Before CUDA 9.1, batched matmul was missing fast FP16 kernels. We
+# check the CUDA version -- if at least 9.1, then put the bmm
+# functions on the fp16 list. Otherwise, put them on the fp32 list.
+_bmms = ['addbmm',
+         'baddbmm',
+         'bmm']
+if utils.get_cuda_version() >= (9, 1, 0):
+    FP16_FUNCS.extend(_bmms)
+else:
+    FP32_FUNCS.extend(_bmms)
+
 # Multi-tensor fns that may need type promotion
 CASTS = [
     # Multi-tensor math
@@ -87,8 +93,9 @@ CASTS = [
     'ne'
 ]
 
-# Will possibly need to promote *all* elements of `seq`
+# Functions that take sequence arguments. We need to inspect the whole
+# sequence and cast to the widest type.
 SEQUENCE_CASTS = [
-    'cat', # torch.cat(seq, dim=0, out=None)
-    'stack' # torch.stack(seq, dim=0, out=None)
+    'cat',
+    'stack'
 ]

apex/amp/utils.py

@@ -5,6 +5,9 @@ import itertools
 
 import torch
 
+def get_cuda_version():
+    return tuple(int(x) for x in torch.version.cuda.split('.'))
+
 def is_fp_tensor(x):
     if is_nested(x):
         # Fast-fail version of all(is_fp_tensor)