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Enable multi-device for some models (#30207)

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* 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 <jackylee328@gmail.com>
This commit is contained in:
Jacky Lee
2024-04-19 01:24:44 -07:00
committed by GitHub
parent ecfe9be705
commit 30b453206d
27 changed files with 42 additions and 4 deletions
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1
src/transformers/models/bit/modeling_bit.py
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@@ -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):

1
src/transformers/models/convnext/modeling_convnext.py
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@@ -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"""

1
src/transformers/models/convnextv2/modeling_convnextv2.py
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@@ -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"""

1
src/transformers/models/cvt/modeling_cvt.py
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@@ -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"""

1
src/transformers/models/donut/modeling_donut_swin.py
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@@ -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"""

1
src/transformers/models/focalnet/modeling_focalnet.py
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@@ -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"""

1
src/transformers/models/glpn/modeling_glpn.py
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@@ -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):

1
src/transformers/models/imagegpt/modeling_imagegpt.py
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@@ -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)

1
src/transformers/models/levit/modeling_levit.py
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@@ -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"""

1
src/transformers/models/maskformer/modeling_maskformer_swin.py
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@@ -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"""

1
src/transformers/models/mgp_str/modeling_mgp_str.py
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@@ -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"""

1
src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py
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@@ -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"""

1
src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py
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@@ -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"""

1
src/transformers/models/mobilevit/modeling_mobilevit.py
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@@ -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"""

1
src/transformers/models/mobilevitv2/modeling_mobilevitv2.py
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@@ -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"""

1
src/transformers/models/poolformer/modeling_poolformer.py
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@@ -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"""

1
src/transformers/models/regnet/modeling_regnet.py
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@@ -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):

1
src/transformers/models/resnet/modeling_resnet.py
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@@ -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):

1
src/transformers/models/sam/modeling_sam.py
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@@ -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

1
src/transformers/models/swiftformer/modeling_swiftformer.py
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@@ -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"""

1
src/transformers/models/swin/modeling_swin.py
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@@ -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"""

1
src/transformers/models/swinv2/modeling_swinv2.py
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@@ -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"""

1
src/transformers/models/timesformer/modeling_timesformer.py
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@@ -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)):

1
src/transformers/models/trocr/modeling_trocr.py
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@@ -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

1
src/transformers/models/upernet/modeling_upernet.py
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@@ -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):

1
src/transformers/models/yolos/modeling_yolos.py
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@@ -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"""

12
tests/test_modeling_common.py
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@@ -2907,6 +2907,9 @@ class ModelTesterMixin:
torch.manual_seed(0)
new_output = new_model(**inputs_dict_class)
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
@@ -2939,6 +2942,9 @@ class ModelTesterMixin:
torch.manual_seed(0)
new_output = new_model(**inputs_dict_class)
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
@@ -2975,6 +2981,9 @@ class ModelTesterMixin:
torch.manual_seed(0)
new_output = new_model(**inputs_dict_class)
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
@@ -3011,6 +3020,9 @@ class ModelTesterMixin:
torch.manual_seed(0)
new_output = new_model(**inputs_dict_class)
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):