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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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20
tests/test_modeling_common.py
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@@ -2907,7 +2907,10 @@ class ModelTesterMixin:
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 @@ class ModelTesterMixin:
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 @@ class ModelTesterMixin:
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 @@ class ModelTesterMixin:
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()