Remove all traces of low_cpu_mem_usage (#38792)

* remove it from all py files

* remove it from the doc

* remove it from examples

* style

* remove traces of _fast_init

* Update test_peft_integration.py

* CIs

tests/generation/test_utils.py

@@ -2337,7 +2337,6 @@ class GenerationTesterMixin:
                 model_eager = model_class.from_pretrained(
                     tmpdirname,
                     torch_dtype=torch.float16,
-                    low_cpu_mem_usage=True,
                     attn_implementation="eager",
                 ).to(torch_device)
                 res_eager = model_eager.generate(**inputs_dict, **generate_kwargs)
@@ -2347,7 +2346,6 @@ class GenerationTesterMixin:
                 model_attn = model_class.from_pretrained(
                     tmpdirname,
                     torch_dtype=torch.float16,
-                    low_cpu_mem_usage=True,
                     attn_implementation=attn_implementation,
                 ).to(torch_device)
                 res_attn = model_attn.generate(**inputs_dict, **generate_kwargs)
@@ -3724,7 +3722,6 @@ class GenerationIntegrationTests(unittest.TestCase):
         processor = AutoProcessor.from_pretrained(model_id)
         model = AutoModelForSpeechSeq2Seq.from_pretrained(
             model_id,
-            low_cpu_mem_usage=True,
             use_safetensors=True,
         )
         model.to(torch_device)
@@ -3743,7 +3740,6 @@ class GenerationIntegrationTests(unittest.TestCase):
         # Load its decoder only version:
         assistant_causal_lm = AutoModelForCausalLM.from_pretrained(
             assistant_distil_model_id,
-            low_cpu_mem_usage=True,
             use_safetensors=True,
         ).to(torch_device)
         self.assertTrue(model.generate(**features, assistant_model=assistant_causal_lm).sum())
@@ -3759,7 +3755,6 @@ class GenerationIntegrationTests(unittest.TestCase):
         # Load its decoder only version:
         assistant_causal_lm = AutoModelForCausalLM.from_pretrained(
             assistant_distil_model_id,
-            low_cpu_mem_usage=True,
             use_safetensors=True,
         ).to(torch_device)
         # It will raise an error as the encoder of the main and assistant model are not compatible: