Refine Bf16 test for deepspeed (#17734)

* Refine BF16 check in CPU/GPU

* Fixes

* Renames

src/transformers/utils/__init__.py

@@ -125,6 +125,8 @@ from .import_utils import (
     is_tokenizers_available,
     is_torch_available,
     is_torch_bf16_available,
+    is_torch_bf16_cpu_available,
+    is_torch_bf16_gpu_available,
     is_torch_cuda_available,
     is_torch_fx_available,
     is_torch_fx_proxy,

src/transformers/utils/import_utils.py

@@ -272,7 +272,7 @@ def is_torch_cuda_available():
         return False
 
 
-def is_torch_bf16_available():
+def is_torch_bf16_gpu_available():
     if not is_torch_available():
         return False
 
@@ -288,30 +288,42 @@ def is_torch_bf16_available():
     # 4. torch.autocast exists
     # XXX: one problem here is that it may give invalid results on mixed gpus setup, so it's
     # really only correct for the 0th gpu (or currently set default device if different from 0)
-    is_torch_gpu_bf16_available = True
-    is_torch_cpu_bf16_available = True
     if version.parse(torch.__version__) < version.parse("1.10"):
-        is_torch_gpu_bf16_available = False
-        is_torch_cpu_bf16_available = False
+        return False
 
     if torch.cuda.is_available() and torch.version.cuda is not None:
         if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8:
-            is_torch_gpu_bf16_available = False
+            return False
         if int(torch.version.cuda.split(".")[0]) < 11:
-            is_torch_gpu_bf16_available = False
+            return False
         if not hasattr(torch.cuda.amp, "autocast"):
-            is_torch_gpu_bf16_available = False
+            return False
     else:
-        is_torch_gpu_bf16_available = False
+        return False
+
+    return True
+
+
+def is_torch_bf16_cpu_available():
+    if not is_torch_available():
+        return False
+
+    import torch
+
+    if version.parse(torch.__version__) < version.parse("1.10"):
+        return False
 
-    # checking CPU
     try:
         # multiple levels of AttributeError depending on the pytorch version so do them all in one check
         _ = torch.cpu.amp.autocast
     except AttributeError:
-        is_torch_cpu_bf16_available = False
+        return False
 
-    return is_torch_cpu_bf16_available or is_torch_gpu_bf16_available
+    return True
+
+
+def is_torch_bf16_available():
+    return is_torch_bf16_cpu_available() or is_torch_bf16_gpu_available()
 
 
 def is_torch_tf32_available():

tests/deepspeed/test_deepspeed.py

@@ -42,7 +42,7 @@ from transformers.testing_utils import (
     slow,
 )
 from transformers.trainer_utils import get_last_checkpoint, set_seed
-from transformers.utils import WEIGHTS_NAME, is_torch_bf16_available
+from transformers.utils import WEIGHTS_NAME, is_torch_bf16_gpu_available
 
 
 if is_torch_available():
@@ -129,7 +129,7 @@ FP16 = "fp16"
 BF16 = "bf16"
 
 stages = [ZERO2, ZERO3]
-if is_torch_bf16_available():
+if is_torch_bf16_gpu_available():
     dtypes = [FP16, BF16]
 else:
     dtypes = [FP16]
@@ -920,7 +920,7 @@ class TestDeepSpeedWithLauncher(TestCasePlus):
     @require_torch_multi_gpu
     @parameterized.expand(["bf16", "fp16", "fp32"])
     def test_inference(self, dtype):
-        if dtype == "bf16" and not is_torch_bf16_available():
+        if dtype == "bf16" and not is_torch_bf16_gpu_available():
             self.skipTest("test requires bfloat16 hardware support")
 
         # this is just inference, so no optimizer should be loaded