Enable multi-device for some models (#30207)

* feat: multidevice for resnet

* feat: yes! resnet

* fix: compare all elements in tuple

* feat: support for regnet

* feat: support for convnextv2

* feat: support for bit

* feat: support for cvt

* feat: add support for focalnet

* feat: support for yolos

* feat: support for glpn

* feat: support for imagegpt

* feat: support for levit

* feat: support for mgp_str

* feat: support for mobilnet_v1

* feat: support for mobilnet_v2

* feat: support for mobilevit

* feat: support for mobilevitv2

* feat: support for poolformer

* fix: copies

* fix: code quality check

* update: upstream changes from main

* fix: consistency check

* feat: support for sam

* feat: support for switchformer

* feat: support for swin

* feat: support for swinv2

* feat: support for timesformer

* feat: suport for trocr

* feat: support for upernet

* fix: check copies

* update: rerun CI

* update: rerun again, maybe

* update: one more rerun

---------

Co-authored-by: Jacky Lee <[email protected]>

src/transformers/models/bit/modeling_bit.py

@@ -658,6 +658,7 @@ class BitPreTrainedModel(PreTrainedModel):
     config_class = BitConfig
     base_model_prefix = "bit"
     main_input_name = "pixel_values"
+    _no_split_modules = ["BitEmbeddings"]
 
     def _init_weights(self, module):
         if isinstance(module, nn.Conv2d):

src/transformers/models/convnext/modeling_convnext.py

@@ -280,6 +280,7 @@ class ConvNextPreTrainedModel(PreTrainedModel):
     config_class = ConvNextConfig
     base_model_prefix = "convnext"
     main_input_name = "pixel_values"
+    _no_split_modules = ["ConvNextLayer"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/convnextv2/modeling_convnextv2.py

@@ -301,6 +301,7 @@ class ConvNextV2PreTrainedModel(PreTrainedModel):
     config_class = ConvNextV2Config
     base_model_prefix = "convnextv2"
     main_input_name = "pixel_values"
+    _no_split_modules = ["ConvNextV2Layer"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/cvt/modeling_cvt.py

@@ -534,6 +534,7 @@ class CvtPreTrainedModel(PreTrainedModel):
     config_class = CvtConfig
     base_model_prefix = "cvt"
     main_input_name = "pixel_values"
+    _no_split_modules = ["CvtLayer"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/donut/modeling_donut_swin.py

@@ -809,6 +809,7 @@ class DonutSwinPreTrainedModel(PreTrainedModel):
     base_model_prefix = "swin"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["DonutSwinStage"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/focalnet/modeling_focalnet.py

@@ -636,6 +636,7 @@ class FocalNetPreTrainedModel(PreTrainedModel):
     base_model_prefix = "focalnet"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["FocalNetStage"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/glpn/modeling_glpn.py

@@ -426,6 +426,7 @@ class GLPNPreTrainedModel(PreTrainedModel):
     config_class = GLPNConfig
     base_model_prefix = "glpn"
     main_input_name = "pixel_values"
+    _no_split_modules = []
 
     # Copied from transformers.models.segformer.modeling_segformer.SegformerPreTrainedModel._init_weights
     def _init_weights(self, module):

src/transformers/models/imagegpt/modeling_imagegpt.py

@@ -491,6 +491,7 @@ class ImageGPTPreTrainedModel(PreTrainedModel):
     base_model_prefix = "transformer"
     main_input_name = "input_ids"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["ImageGPTBlock"]
 
     def __init__(self, *inputs, **kwargs):
         super().__init__(*inputs, **kwargs)

src/transformers/models/levit/modeling_levit.py

@@ -491,6 +491,7 @@ class LevitPreTrainedModel(PreTrainedModel):
     config_class = LevitConfig
     base_model_prefix = "levit"
     main_input_name = "pixel_values"
+    _no_split_modules = ["LevitResidualLayer"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/maskformer/modeling_maskformer_swin.py

@@ -735,6 +735,7 @@ class MaskFormerSwinPreTrainedModel(PreTrainedModel):
     base_model_prefix = "model"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["MaskFormerSwinStage"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/mgp_str/modeling_mgp_str.py

@@ -317,6 +317,7 @@ class MgpstrPreTrainedModel(PreTrainedModel):
 
     config_class = MgpstrConfig
     base_model_prefix = "mgp_str"
+    _no_split_modules = []
 
     def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
         """Initialize the weights"""

src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py

@@ -254,6 +254,7 @@ class MobileNetV1PreTrainedModel(PreTrainedModel):
     base_model_prefix = "mobilenet_v1"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = False
+    _no_split_modules = []
 
     def _init_weights(self, module: Union[nn.Linear, nn.Conv2d]) -> None:
         """Initialize the weights"""

src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py

@@ -453,6 +453,7 @@ class MobileNetV2PreTrainedModel(PreTrainedModel):
     base_model_prefix = "mobilenet_v2"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = False
+    _no_split_modules = []
 
     def _init_weights(self, module: Union[nn.Linear, nn.Conv2d]) -> None:
         """Initialize the weights"""

src/transformers/models/mobilevit/modeling_mobilevit.py

@@ -644,6 +644,7 @@ class MobileViTPreTrainedModel(PreTrainedModel):
     base_model_prefix = "mobilevit"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["MobileViTLayer"]
 
     def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
         """Initialize the weights"""

src/transformers/models/mobilevitv2/modeling_mobilevitv2.py

@@ -606,6 +606,7 @@ class MobileViTV2PreTrainedModel(PreTrainedModel):
     base_model_prefix = "mobilevitv2"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["MobileViTV2Layer"]
 
     def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
         """Initialize the weights"""

src/transformers/models/poolformer/modeling_poolformer.py

@@ -268,6 +268,7 @@ class PoolFormerPreTrainedModel(PreTrainedModel):
     config_class = PoolFormerConfig
     base_model_prefix = "poolformer"
     main_input_name = "pixel_values"
+    _no_split_modules = ["PoolFormerLayer"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/regnet/modeling_regnet.py

@@ -281,6 +281,7 @@ class RegNetPreTrainedModel(PreTrainedModel):
     config_class = RegNetConfig
     base_model_prefix = "regnet"
     main_input_name = "pixel_values"
+    _no_split_modules = ["RegNetYLayer"]
 
     # Copied from transformers.models.resnet.modeling_resnet.ResNetPreTrainedModel._init_weights
     def _init_weights(self, module):

src/transformers/models/resnet/modeling_resnet.py

@@ -272,6 +272,7 @@ class ResNetPreTrainedModel(PreTrainedModel):
     config_class = ResNetConfig
     base_model_prefix = "resnet"
     main_input_name = "pixel_values"
+    _no_split_modules = ["ResNetConvLayer", "ResNetShortCut"]
 
     def _init_weights(self, module):
         if isinstance(module, nn.Conv2d):

src/transformers/models/sam/modeling_sam.py

@@ -1074,6 +1074,7 @@ class SamPreTrainedModel(PreTrainedModel):
     config_class = SamConfig
     base_model_prefix = "sam"
     main_input_name = "pixel_values"
+    _no_split_modules = ["SamVisionAttention"]
 
     def _init_weights(self, module):
         std = self.config.initializer_range

src/transformers/models/swiftformer/modeling_swiftformer.py

@@ -428,6 +428,7 @@ class SwiftFormerPreTrainedModel(PreTrainedModel):
     base_model_prefix = "swiftformer"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["SwiftFormerEncoderBlock"]
 
     def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
         """Initialize the weights"""

src/transformers/models/swin/modeling_swin.py

@@ -884,6 +884,7 @@ class SwinPreTrainedModel(PreTrainedModel):
     base_model_prefix = "swin"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["SwinStage"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/swinv2/modeling_swinv2.py

@@ -939,6 +939,7 @@ class Swinv2PreTrainedModel(PreTrainedModel):
     base_model_prefix = "swinv2"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["Swinv2Stage"]
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/timesformer/modeling_timesformer.py

@@ -472,6 +472,7 @@ class TimesformerPreTrainedModel(PreTrainedModel):
     base_model_prefix = "timesformer"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["TimesformerLayer"]
 
     def _init_weights(self, module):
         if isinstance(module, (nn.Linear, nn.Conv2d)):

src/transformers/models/trocr/modeling_trocr.py

@@ -407,6 +407,7 @@ class TrOCRPreTrainedModel(PreTrainedModel):
     config_class = TrOCRConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
+    _no_split_modules = ["TrOCRDecoderLayer"]
 
     def _init_weights(self, module):
         std = self.config.init_std

src/transformers/models/upernet/modeling_upernet.py

@@ -293,6 +293,7 @@ class UperNetPreTrainedModel(PreTrainedModel):
 
     config_class = UperNetConfig
     main_input_name = "pixel_values"
+    _no_split_modules = []
 
     def _init_weights(self, module):
         if isinstance(module, UperNetPreTrainedModel):

src/transformers/models/yolos/modeling_yolos.py

@@ -533,6 +533,7 @@ class YolosPreTrainedModel(PreTrainedModel):
     base_model_prefix = "vit"
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
+    _no_split_modules = []
 
     def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
         """Initialize the weights"""

tests/test_modeling_common.py

@@ -2907,7 +2907,10 @@ def test_disk_offload_bin(self):
                 torch.manual_seed(0)
                 new_output = new_model(**inputs_dict_class)
 
-                self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
+                if isinstance(base_output[0], tuple) and isinstance(new_output[0], tuple):
+                    self.assertTrue(torch.allclose(a, b, atol=1e-5) for a, b in zip(base_output[0], new_output[0]))
+                else:
+                    self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
 
     @require_accelerate
     @mark.accelerate_tests
@@ -2939,7 +2942,10 @@ def test_disk_offload_safetensors(self):
                 torch.manual_seed(0)
                 new_output = new_model(**inputs_dict_class)
 
-                self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
+                if isinstance(base_output[0], tuple) and isinstance(new_output[0], tuple):
+                    self.assertTrue(torch.allclose(a, b, atol=1e-5) for a, b in zip(base_output[0], new_output[0]))
+                else:
+                    self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
 
     @require_accelerate
     @mark.accelerate_tests
@@ -2975,7 +2981,10 @@ def test_cpu_offload(self):
                     torch.manual_seed(0)
                     new_output = new_model(**inputs_dict_class)
 
-                    self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
+                    if isinstance(base_output[0], tuple) and isinstance(new_output[0], tuple):
+                        self.assertTrue(torch.allclose(a, b, atol=1e-5) for a, b in zip(base_output[0], new_output[0]))
+                    else:
+                        self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
 
     @require_accelerate
     @mark.accelerate_tests
@@ -3011,7 +3020,10 @@ def test_model_parallelism(self):
                     torch.manual_seed(0)
                     new_output = new_model(**inputs_dict_class)
 
-                    self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
+                    if isinstance(base_output[0], tuple) and isinstance(new_output[0], tuple):
+                        self.assertTrue(torch.allclose(a, b, atol=1e-5) for a, b in zip(base_output[0], new_output[0]))
+                    else:
+                        self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
 
     def test_problem_types(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()