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Merged
merged 2 commits into from
Dec 21, 2023
Merged

Support more peft tuners #245

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230d29a
support more tuners
tastelikefeet Dec 21, 2023
7aa51c0
fix
tastelikefeet Dec 21, 2023
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24 changes: 13 additions & 11 deletions swift/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,14 +7,15 @@
from .version import __version__, __release_datetime__
from .tuners import (
Adapter, AdapterConfig, AdapterModule, SwiftModel, LoRA, LoRAConfig,
SWIFT_MAPPING, LoraConfig, PeftConfig, PeftModel, PeftModelForCausalLM,
ResTuningConfig, SideConfig, PeftModelForSeq2SeqLM,
PeftModelForSequenceClassification, PeftModelForTokenClassification,
PrefixTuningConfig, PromptEncoderConfig, PromptLearningConfig,
PromptTuningConfig, get_peft_config, get_peft_model,
get_peft_model_state_dict, Prompt, PromptConfig, PromptModule,
SwiftConfig, SwiftOutput, Swift, SwiftTuners, LongLoRAConfig, LongLoRA,
LongLoRAModelType)
SWIFT_MAPPING, AdaLoraConfig, IA3Config, LoftQConfig, LoHaConfig,
LoKrConfig, LoraConfig, OFTConfig, PeftConfig, PeftModel,
PeftModelForCausalLM, ResTuningConfig, SideConfig,
PeftModelForSeq2SeqLM, PeftModelForSequenceClassification,
PeftModelForTokenClassification, PrefixTuningConfig,
PromptEncoderConfig, PromptLearningConfig, PromptTuningConfig,
get_peft_config, get_peft_model, get_peft_model_state_dict, Prompt,
PromptConfig, PromptModule, SwiftConfig, SwiftOutput, Swift,
SwiftTuners, LongLoRAConfig, LongLoRA, LongLoRAModelType)
from .hub import snapshot_download, push_to_hub, push_to_hub_async, push_to_hub_in_queue
from .trainers import (EvaluationStrategy, FSDPOption, HPSearchBackend,
HubStrategy, IntervalStrategy, SchedulerType,
Expand All @@ -30,9 +31,10 @@
],
'tuners': [
'Adapter', 'AdapterConfig', 'AdapterModule', 'SwiftModel', 'LoRA',
'LoRAConfig', 'SWIFT_MAPPING', 'LoraConfig', 'PeftConfig',
'ResTuningConfig', 'SideConfig', 'PeftModel',
'PeftModelForCausalLM', 'PeftModelForSeq2SeqLM',
'LoRAConfig', 'SWIFT_MAPPING', 'LoraConfig', 'AdaLoraConfig',
'IA3Config', 'LoftQConfig', 'LoHaConfig', 'LoKrConfig',
'OFTConfig', 'PeftConfig', 'ResTuningConfig', 'SideConfig',
'PeftModel', 'PeftModelForCausalLM', 'PeftModelForSeq2SeqLM',
'PeftModelForSequenceClassification',
'PeftModelForTokenClassification', 'PrefixTuningConfig',
'PromptEncoderConfig', 'PromptLearningConfig',
Expand Down
11 changes: 7 additions & 4 deletions swift/tuners/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,9 @@
from .neftune import NEFTune, NEFTuneConfig
from .longlora.longlora import LongLoRAModelType, LongLoRAConfig, LongLoRA
from .restuning import ResTuning, ResTuningConfig, ResTuningBypassModule
from .peft import (LoraConfig, PeftConfig, PeftModel, PeftModelForCausalLM,
PeftModelForSeq2SeqLM,
from .peft import (AdaLoraConfig, IA3Config, LoftQConfig, LoHaConfig,
LoKrConfig, LoraConfig, OFTConfig, PeftConfig,
PeftModel, PeftModelForCausalLM, PeftModelForSeq2SeqLM,
PeftModelForSequenceClassification,
PeftModelForTokenClassification, PrefixTuningConfig,
PromptEncoderConfig, PromptLearningConfig,
Expand All @@ -33,8 +34,10 @@
'neftune': ['NEFTune', 'NEFTuneConfig'],
'restuning': ['ResTuning', 'ResTuningConfig', 'ResTuningBypassModule'],
'peft': [
'LoraConfig', 'PeftConfig', 'PeftModel', 'PeftModelForCausalLM',
'PeftModelForSeq2SeqLM', 'PeftModelForSequenceClassification',
'AdaLoraConfig', 'IA3Config', 'LoftQConfig', 'LoHaConfig',
'LoKrConfig', 'LoraConfig', 'OFTConfig', 'PeftConfig', 'PeftModel',
'PeftModelForCausalLM', 'PeftModelForSeq2SeqLM',
'PeftModelForSequenceClassification',
'PeftModelForTokenClassification', 'PrefixTuningConfig',
'PromptEncoderConfig', 'PromptLearningConfig',
'PromptTuningConfig', 'get_peft_config', 'get_peft_model',
Expand Down
12 changes: 10 additions & 2 deletions swift/tuners/peft.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,10 @@
import os.path
from typing import Optional

from peft import (LoraConfig, PeftConfig, PeftModel, PeftModelForCausalLM,
PeftModelForSeq2SeqLM, PeftModelForSequenceClassification,
from peft import (AdaLoraConfig, IA3Config, LoftQConfig, LoHaConfig,
LoKrConfig, LoraConfig, OFTConfig, PeftConfig, PeftModel,
PeftModelForCausalLM, PeftModelForSeq2SeqLM,
PeftModelForSequenceClassification,
PeftModelForTokenClassification, PrefixTuningConfig,
PromptEncoderConfig, PromptLearningConfig,
PromptTuningConfig, get_peft_config, get_peft_model,
Expand Down Expand Up @@ -60,6 +62,12 @@ def wrap_module(module):
PrefixTuningConfig = wrap_module(PrefixTuningConfig)
PromptLearningConfig = wrap_module(PromptLearningConfig)
LoraConfig = wrap_module(LoraConfig)
AdaLoraConfig = wrap_module(AdaLoraConfig)
IA3Config = wrap_module(IA3Config)
LoHaConfig = wrap_module(LoHaConfig)
LoKrConfig = wrap_module(LoKrConfig)
LoftQConfig = wrap_module(LoftQConfig)
OFTConfig = wrap_module(OFTConfig)
get_peft_config = get_peft_config
get_peft_model_state_dict = get_peft_model_state_dict
get_peft_model = get_peft_model
23 changes: 22 additions & 1 deletion tests/tuners/test_peft.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
SbertForSequenceClassification)
from peft.utils import WEIGHTS_NAME

from swift import LoraConfig, Swift
from swift import AdaLoraConfig, LoraConfig, Swift


class TestPeft(unittest.TestCase):
Expand Down Expand Up @@ -44,3 +44,24 @@ def test_peft_lora_injection(self):
all(
torch.isclose(state_dict[key],
state_dict2[key]).flatten().detach().cpu()))

def test_peft_adalora_injection(self):
model = SbertForSequenceClassification(SbertConfig())
model2 = copy.deepcopy(model)
adalora_config = AdaLoraConfig(
target_modules=['query', 'key', 'value'])
model = Swift.prepare_model(model, adalora_config)
model.save_pretrained(self.tmp_dir, safe_serialization=False)
with open(os.path.join(self.tmp_dir, 'configuration.json'), 'w') as f:
f.write('{}')
self.assertTrue(
os.path.exists(os.path.join(self.tmp_dir, WEIGHTS_NAME)))
model2 = Swift.from_pretrained(model2, self.tmp_dir)
state_dict = model.state_dict()
state_dict2 = model2.state_dict()
for key in state_dict:
self.assertTrue(key in state_dict2)
self.assertTrue(
all(
torch.isclose(state_dict[key],
state_dict2[key]).flatten().detach().cpu()))