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Add missing None check for hf_quantizer #28804

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merged 6 commits into from
Feb 2, 2024
Merged

Add missing None check for hf_quantizer #28804

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9acd939
Add missing None check for hf_quantizer
jganitkevitch Feb 1, 2024
780ea90
Add test, fix logic.
jganitkevitch Feb 1, 2024
aac8f77
make style
jganitkevitch Feb 1, 2024
9b822bc
Switch test model to Mistral
jganitkevitch Feb 2, 2024
85d6a39
Comment
jganitkevitch Feb 2, 2024
60f9d9c
Update tests/test_modeling_utils.py
younesbelkada Feb 2, 2024
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10 changes: 6 additions & 4 deletions src/transformers/modeling_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3727,10 +3727,12 @@ def _fix_key(key):

if param.device == torch.device("meta"):
value = torch.empty(*param.size(), dtype=target_dtype)
if getattr(
hf_quantizer, "requires_parameters_quantization", False
) or not hf_quantizer.check_quantized_param(
model, param_value=value, param_name=key, state_dict={}
if (
hf_quantizer is None
or getattr(hf_quantizer, "requires_parameters_quantization", False)
or not hf_quantizer.check_quantized_param(
model, param_value=value, param_name=key, state_dict={}
)
):
set_module_tensor_to_device(model, key, "cpu", value)
else:
Expand Down
12 changes: 12 additions & 0 deletions tests/test_modeling_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@
from transformers import (
AutoConfig,
AutoModel,
AutoModelForSequenceClassification,
OwlViTForObjectDetection,
PretrainedConfig,
is_torch_available,
Expand Down Expand Up @@ -201,6 +202,7 @@ def forward(self, mask, inputs_embeds):

TINY_T5 = "patrickvonplaten/t5-tiny-random"
TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
TINY_MISTRAL = "hf-internal-testing/tiny-random-MistralForCausalLM"


def check_models_equal(model1, model2):
Expand Down Expand Up @@ -300,6 +302,16 @@ def test_model_from_pretrained_with_different_pretrained_model_name(self):
BertModel.from_pretrained(TINY_T5)
self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)

@require_accelerate
def test_model_from_pretrained_with_none_quantization_config(self):
model = None
# Needs a device_map for to enter the low_cpu_mem branch. We also load AutoModelForSequenceClassification
# deliberately to enter the missing keys branch.
model = AutoModelForSequenceClassification.from_pretrained(
TINY_MISTRAL, device_map="auto", quantization_config=None
)
self.assertIsNotNone(model)

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
Expand Down
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