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Add variant to transformers (#21332)

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* Bump onnx in /examples/research_projects/decision_transformer

Bumps [onnx](https://github.com/onnx/onnx) from 1.11.0 to 1.13.0.
- [Release notes](https://github.com/onnx/onnx/releases)
- [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog.md)
- [Commits](https://github.com/onnx/onnx/compare/v1.11.0...v1.13.0)

---
updated-dependencies:
- dependency-name: onnx
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

* adapt

* finish

* Update examples/research_projects/decision_transformer/requirements.txt

* up

* add tests

* Apply suggestions from code review

Co-authored-by: Lucain <lucainp@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* fix test

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Lucain <lucainp@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
This commit is contained in:
Patrick von Platen
2023-02-01 10:21:52 +02:00
committed by GitHub
parent bc44e947f3
commit 90cddfa824
2 changed files with 213 additions and 34 deletions
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132
tests/test_modeling_common.py
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@@ -2958,6 +2958,138 @@ class ModelUtilsTest(TestCasePlus):
for p1, p2 in zip(model.parameters(), ref_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2")
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local_sharded(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB")
weights_index_name = ".".join(WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
for i in range(1, 6):
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00006"] + ["bin"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", safe_serialization=True)
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["safetensors"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_sharded_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB", safe_serialization=True)
weights_index_name = ".".join(SAFE_WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
for i in range(1, 6):
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00006"] + ["safetensors"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
def test_checkpoint_variant_hub_sharded(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_sharded_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_accelerate
def test_from_pretrained_low_cpu_mem_usage_functional(self):
# test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and