finish all todos (#1957)

tests/test_big_modeling.py

@@ -638,22 +638,16 @@ def test_dispatch_model_bnb(self):
         with init_empty_weights():
             model = AutoModel.from_config(AutoConfig.from_pretrained("bigscience/bloom-560m"))
 
-        # TODO: @younesbelkada remove the positional arg on the next `transformers` release
         quantization_config = BitsAndBytesConfig(load_in_8bit=True)
         model = replace_with_bnb_linear(
             model, modules_to_not_convert=["lm_head"], quantization_config=quantization_config
         )
 
-        # TODO: @younesbelkada remove this block on the next `transformers` release
-        for p in model.parameters():
-            p.requires_grad = False
-
         model_path = hf_hub_download("bigscience/bloom-560m", "pytorch_model.bin")
 
         model = load_checkpoint_and_dispatch(
             model,
             checkpoint=model_path,
-            # device_map="auto",
             device_map="balanced",
         )
 
@@ -674,16 +668,11 @@ def test_dispatch_model_int8_simple(self):
         with init_empty_weights():
             model = AutoModel.from_config(AutoConfig.from_pretrained("bigscience/bloom-560m"))
 
-        # TODO: @younesbelkada remove the positional arg on the next `transformers` release
         quantization_config = BitsAndBytesConfig(load_in_8bit=True)
         model = replace_with_bnb_linear(
             model, modules_to_not_convert=["lm_head"], quantization_config=quantization_config
         )
 
-        # TODO: @younesbelkada remove this block on the next `transformers` release
-        for p in model.parameters():
-            p.requires_grad = False
-
         model_path = hf_hub_download("bigscience/bloom-560m", "pytorch_model.bin")
 
         # test with auto
@@ -699,14 +688,10 @@ def test_dispatch_model_int8_simple(self):
         with init_empty_weights():
             model = AutoModel.from_config(AutoConfig.from_pretrained("bigscience/bloom-560m"))
 
-        # TODO: @younesbelkada remove the positional arg on the next `transformers` release
         model = replace_with_bnb_linear(
             model, modules_to_not_convert=["lm_head"], quantization_config=quantization_config
         )
 
-        for p in model.parameters():
-            p.requires_grad = False
-
         # test with str device map
         model = load_checkpoint_and_dispatch(
             model,
@@ -720,15 +705,10 @@ def test_dispatch_model_int8_simple(self):
         with init_empty_weights():
             model = AutoModel.from_config(AutoConfig.from_pretrained("bigscience/bloom-560m"))
 
-        # TODO: @younesbelkada remove the positional arg on the next `transformers` release
         model = replace_with_bnb_linear(
             model, modules_to_not_convert=["lm_head"], quantization_config=quantization_config
         )
 
-        # TODO: @younesbelkada remove this block on the next `transformers` release
-        for p in model.parameters():
-            p.requires_grad = False
-
         # test with torch.device device map
         model = load_checkpoint_and_dispatch(
             model,
@@ -741,7 +721,6 @@ def test_dispatch_model_int8_simple(self):
 
     @slow
     @require_bnb
-    @unittest.skip("Un-skip in the next transformers release")
     def test_dipatch_model_fp4_simple(self):
         """Tests that `dispatch_model` quantizes fp4 layers"""
         from huggingface_hub import hf_hub_download