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[from_pretrained] extend torch_dtype="auto" to look up config.torch_dtype first, expand docs (#21524)

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* [from_pretrained] expand on torch_dtype entry

* fold 4 into 1

* style

* support torch_dtype='config' plus tests

* style

* oops

* fold config into auto, fix bug

* fix check

* better log

* better log

* clean up
This commit is contained in:
Stas Bekman
2023-02-10 09:09:21 -08:00
committed by GitHub
parent 9e40bba6ba
commit 2f5507580b
3 changed files with 78 additions and 20 deletions
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32
tests/test_modeling_common.py
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@@ -2785,7 +2785,6 @@ class ModelUtilsTest(TestCasePlus):
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
@require_torch
def test_model_from_config_torch_dtype(self):
# test that the model can be instantiated with dtype of user's choice - as long as it's a
# float dtype. To make it happen config.torch_dtype needs to be set before instantiating the
@@ -2804,7 +2803,6 @@ class ModelUtilsTest(TestCasePlus):
with self.assertRaises(ValueError):
model = AutoModel.from_config(config, torch_dtype=torch.int64)
@require_torch
def test_model_from_pretrained_torch_dtype(self):
# test that the model can be instantiated with dtype of either
# 1. explicit from_pretrained's torch_dtype argument
@@ -2818,11 +2816,25 @@ class ModelUtilsTest(TestCasePlus):
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
self.assertEqual(model.dtype, torch.float32)
def remove_torch_dtype(model_path):
file = f"{model_path}/config.json"
with open(file, "r", encoding="utf-8") as f:
s = json.load(f)
s.pop("torch_dtype")
with open(file, "w", encoding="utf-8") as f:
json.dump(s, f)
# test the default fp32 save_pretrained => from_pretrained cycle
model.save_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
self.assertEqual(model.dtype, torch.float32)
# test with auto-detection
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
# 2. test torch_dtype="auto" via auto-derivation
# now remove the torch_dtype entry from config.json and try "auto" again which should
# perform auto-derivation from weights
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
@@ -2833,24 +2845,32 @@ class ModelUtilsTest(TestCasePlus):
# test fp16 save_pretrained, loaded with auto-detection
model = model.half()
model.save_pretrained(model_path)
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.config.torch_dtype, torch.float16)
self.assertEqual(model.dtype, torch.float16)
# tests `config.torch_dtype` saving
with open(f"{model_path}/config.json") as f:
config_dict = json.load(f)
self.assertEqual(config_dict["torch_dtype"], "float16")
# 2. test torch_dtype="auto" via auto-derivation
# now same with using config info
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float16)
# test fp16 save_pretrained, loaded with the explicit fp16
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test AutoModel separately as it goes through a different path
# test auto-detection
# test auto-detection - as currently TINY_T5 doesn't have torch_dtype entry
model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto")
# test that the config object didn't get polluted with torch_dtype="auto"
# there was a bug that after this call we ended up with config.torch_dtype=="auto"
self.assertNotEqual(model.config.torch_dtype, "auto")
# now test the outcome
self.assertEqual(model.dtype, torch.float32)
# test forcing an explicit dtype
model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)