enable cpu offloading for Bark on xpu (#37599)

* enable cpu offloading of bark modeling on XPU

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* remove debug print

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* fix style

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* fix review comments

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* enhance test

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* update

* add deprecate message

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* update

* update

* trigger CI

---------

Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/bark/modeling_bark.py

@@ -15,6 +15,7 @@
 """PyTorch BARK model."""
 
 import math
+import warnings
 from typing import Dict, Optional, Tuple, Union
 
 import numpy as np
@@ -36,6 +37,7 @@ from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     is_accelerate_available,
+    is_torch_accelerator_available,
     logging,
 )
 from ..auto import AutoModel
@@ -1598,26 +1600,45 @@ class BarkModel(BarkPreTrainedModel):
             ):
                 return torch.device(module._hf_hook.execution_device)
 
-    def enable_cpu_offload(self, gpu_id: Optional[int] = 0):
+    def enable_cpu_offload(
+        self,
+        accelerator_id: Optional[int] = 0,
+        **kwargs,
+    ):
         r"""
         Offloads all sub-models to CPU using accelerate, reducing memory usage with a low impact on performance. This
-        method moves one whole sub-model at a time to the GPU when it is used, and the sub-model remains in GPU until
-        the next sub-model runs.
+        method moves one whole sub-model at a time to the accelerator when it is used, and the sub-model remains in accelerator until the next sub-model runs.
 
         Args:
-            gpu_id (`int`, *optional*, defaults to 0):
-                GPU id on which the sub-models will be loaded and offloaded.
+            accelerator_id (`int`, *optional*, defaults to 0):
+                accelerator id on which the sub-models will be loaded and offloaded. This argument is deprecated.
+            kwargs (`dict`, *optional*):
+                additional keyword arguments:
+                    `gpu_id`: accelerator id on which the sub-models will be loaded and offloaded.
         """
         if is_accelerate_available():
             from accelerate import cpu_offload_with_hook
         else:
             raise ImportError("`enable_model_cpu_offload` requires `accelerate`.")
 
-        device = torch.device(f"cuda:{gpu_id}")
+        gpu_id = kwargs.get("gpu_id", 0)
 
+        if gpu_id != 0:
+            warnings.warn(
+                "The argument `gpu_id` is deprecated and will be removed in version 4.54.0 of Transformers. Please use `accelerator_id` instead.",
+                FutureWarning,
+            )
+            accelerator_id = gpu_id
+
+        device_type = "cuda"
+        if is_torch_accelerator_available():
+            device_type = torch.accelerator.current_accelerator().type
+        device = torch.device(f"{device_type}:{accelerator_id}")
+
+        torch_accelerator_module = getattr(torch, device_type)
         if self.device.type != "cpu":
             self.to("cpu")
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+            torch_accelerator_module.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
 
         # this layer is used outside the first foward pass of semantic so need to be loaded before semantic
         self.semantic.input_embeds_layer, _ = cpu_offload_with_hook(self.semantic.input_embeds_layer, device)

src/transformers/utils/__init__.py

@@ -211,6 +211,7 @@ from .import_utils import (
     is_tiktoken_available,
     is_timm_available,
     is_tokenizers_available,
+    is_torch_accelerator_available,
     is_torch_available,
     is_torch_bf16_available,
     is_torch_bf16_available_on_device,

src/transformers/utils/import_utils.py

@@ -346,16 +346,28 @@ def is_accelerate_available(min_version: str = ACCELERATE_MIN_VERSION):
     return _accelerate_available and version.parse(_accelerate_version) >= version.parse(min_version)
 
 
+def is_torch_accelerator_available():
+    if is_torch_available():
+        import torch
+
+        return hasattr(torch, "accelerator")
+
+    return False
+
+
 def is_torch_deterministic():
     """
     Check whether pytorch uses deterministic algorithms by looking if torch.set_deterministic_debug_mode() is set to 1 or 2"
     """
-    import torch
+    if is_torch_available():
+        import torch
 
-    if torch.get_deterministic_debug_mode() == 0:
-        return False
-    else:
-        return True
+        if torch.get_deterministic_debug_mode() == 0:
+            return False
+        else:
+            return True
+
+    return False
 
 
 def is_hadamard_available():

tests/models/bark/test_modeling_bark.py

@@ -36,6 +36,7 @@ from transformers.models.bark.generation_configuration_bark import (
 from transformers.testing_utils import (
     require_flash_attn,
     require_torch,
+    require_torch_accelerator,
     require_torch_fp16,
     require_torch_gpu,
     slow,
@@ -1056,7 +1057,8 @@ class BarkModelIntegrationTests(unittest.TestCase):
     def inputs(self):
         input_ids = self.processor("In the light of the moon, a little egg lay on a leaf", voice_preset="en_speaker_6")
 
-        input_ids = input_ids.to(torch_device)
+        for k, v in input_ids.items():
+            input_ids[k] = v.to(torch_device)
 
         return input_ids
 
@@ -1295,7 +1297,7 @@ class BarkModelIntegrationTests(unittest.TestCase):
             len(output_ids_with_min_eos_p[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist())
         )
 
-    @require_torch_gpu
+    @require_torch_accelerator
     @slow
     def test_generate_end_to_end_with_offload(self):
         input_ids = self.inputs
@@ -1304,15 +1306,17 @@ class BarkModelIntegrationTests(unittest.TestCase):
             # standard generation
             output_with_no_offload = self.model.generate(**input_ids, do_sample=False, temperature=1.0)
 
-            torch.cuda.empty_cache()
+            torch_accelerator_module = getattr(torch, torch_device, torch.cuda)
 
-            memory_before_offload = torch.cuda.memory_allocated()
+            torch_accelerator_module.empty_cache()
+
+            memory_before_offload = torch_accelerator_module.memory_allocated()
             model_memory_footprint = self.model.get_memory_footprint()
 
             # activate cpu offload
             self.model.enable_cpu_offload()
 
-            memory_after_offload = torch.cuda.memory_allocated()
+            memory_after_offload = torch_accelerator_module.memory_allocated()
 
             # checks if the model have been offloaded