Fix torch_dtype in lite (#2956)

InternLM · Dec 26, 2024 · 191a7dd · 191a7dd
1 parent 3a98ae9
commit 191a7dd
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Showing 2 changed files with 20 additions and 5 deletions.
diff --git a/lmdeploy/lite/apis/calibrate.py b/lmdeploy/lite/apis/calibrate.py
@@ -262,7 +262,13 @@ def calibrate(model: str,
         if dtype == 'float16':
             model.half()
         elif dtype == 'bfloat16':
+            assert torch.cuda.is_bf16_supported(
+            ), 'your device does not support bfloat16 please set --dtype float16'  # noqa
             model.to(torch.bfloat16)
+        elif dtype == 'auto' and model.config.torch_dtype == torch.bfloat16:
+            print('Warning: we cast model to float16 to prevent OOM. You'
+                  ' may enforce it bfloat16 by `--dtype bfloat16`')
+            model.half()
         model.eval()
 
     model_type = type(model).__name__

diff --git a/lmdeploy/lite/utils/load.py b/lmdeploy/lite/utils/load.py
@@ -12,14 +12,13 @@ def load_hf_from_pretrained(pretrained_model_name_or_path,
                             dtype: Literal['float16', 'bfloat16',
                                            'auto'], **kwargs):
 
-    if dtype == torch.bfloat16 and not torch.cuda.is_bf16_supported():
+    if dtype == 'bfloat16' and not torch.cuda.is_bf16_supported():
         raise RuntimeError('Your device does not supports bf16(bfloat16), '
                            'please change to fp16(float16)')
 
     kwargs.pop('config', None)
 
     hf_config = AutoConfig.from_pretrained(pretrained_model_name_or_path,
-                                           torch_dtype=dtype,
                                            trust_remote_code=True)
 
     # HACK hard code for qwen, other configs do not have the `fp16` attribute.
@@ -29,13 +28,23 @@ def load_hf_from_pretrained(pretrained_model_name_or_path,
         else:
             hf_config.fp16 = True
 
-    if dtype != 'auto':
-        setattr(hf_config, 'torch_dtype', dtype)
+    torch_dtype = getattr(hf_config, 'torch_dtype', torch.float16)
+    if dtype == 'bfloat16':
+        torch_dtype = torch.bfloat16
+    elif dtype == 'float16':
+        torch_dtype = torch.float16
+    elif dtype == 'auto' and torch_dtype == torch.bfloat16:
+        print('Warning: we cast model to float16 to prevent OOM. '
+              'You may enforce it bfloat16 by `--dtype bfloat16`')
+        torch_dtype = torch.float16
 
     with LoadNoInit():
         # Load model
         model = AutoModelForCausalLM.from_pretrained(
-            pretrained_model_name_or_path, config=hf_config, **kwargs)
+            pretrained_model_name_or_path,
+            config=hf_config,
+            torch_dtype=torch_dtype,
+            **kwargs)
         model.config.use_cache = False
 
     return model