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Fix Inconsistent Missing Keys Warning for Adapter Weights in PEFT #2084

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Merged
merged 9 commits into from
Sep 25, 2024
Merged

Fix Inconsistent Missing Keys Warning for Adapter Weights in PEFT #2084

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9c2e8fa
added logic to filter missing keys
yaswanth19 Sep 21, 2024
f9aff6d
🔧 Added peft_type_tp_prefix_mapping
yaswanth19 Sep 23, 2024
ba2ab8c
removed warnings from laod_adapter
yaswanth19 Sep 23, 2024
55c1c58
added logic to filter missing keys
yaswanth19 Sep 21, 2024
68f342a
added check on adapter name
yaswanth19 Sep 24, 2024
2077376
Added test cases for filtering missing key logic
yaswanth19 Sep 24, 2024
22c7b13
Minor refactoring
yaswanth19 Sep 24, 2024
31536ac
Modified test case
yaswanth19 Sep 25, 2024
53395ef
formatting fix
yaswanth19 Sep 25, 2024
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15 changes: 14 additions & 1 deletion src/peft/peft_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@
from transformers.modeling_outputs import QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from transformers.utils import PushToHubMixin

from peft.utils.constants import DUMMY_MODEL_CONFIG
from peft.utils.constants import DUMMY_MODEL_CONFIG, PEFT_TYPE_TO_PREFIX_MAPPING

from . import __version__
from .config import PeftConfig
Expand Down Expand Up @@ -1185,6 +1185,19 @@ def load_adapter(
ignore_mismatched_sizes=ignore_mismatched_sizes,
low_cpu_mem_usage=low_cpu_mem_usage,
)

tuner = self.peft_config[adapter_name].peft_type
tuner_prefix = PEFT_TYPE_TO_PREFIX_MAPPING.get(tuner, "")
adapter_missing_keys = []

# Filter missing keys specific to the current adapter and tuner prefix.
for key in load_result.missing_keys:
if tuner_prefix in key and adapter_name in key:
adapter_missing_keys.append(key)

load_result.missing_keys.clear()
load_result.missing_keys.extend(adapter_missing_keys)

if (
(getattr(self, "hf_device_map", None) is not None)
and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
Expand Down
18 changes: 18 additions & 0 deletions src/peft/utils/constants.py
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Expand Up @@ -15,6 +15,8 @@
import torch
from transformers import BloomPreTrainedModel

from .peft_types import PeftType


# needed for prefix-tuning of bloom model
def bloom_model_postprocess_past_key_value(past_key_values):
Expand Down Expand Up @@ -284,6 +286,22 @@ def starcoder_model_postprocess_past_key_value(past_key_values):
"qwen2": ["q_proj", "v_proj"],
}

PEFT_TYPE_TO_PREFIX_MAPPING = {
PeftType.IA3: "ia3_",
PeftType.LORA: "lora_",
PeftType.ADALORA: "lora_",
PeftType.LOHA: "hada_",
PeftType.LOKR: "lokr_",
PeftType.OFT: "oft_",
PeftType.POLY: "poly_",
PeftType.BOFT: "boft_",
PeftType.LN_TUNING: "ln_tuning_",
PeftType.VERA: "vera_lambda_",
PeftType.FOURIERFT: "fourierft_",
PeftType.HRA: "hra_",
PeftType.VBLORA: "vblora_",
}

WEIGHTS_NAME = "adapter_model.bin"
SAFETENSORS_WEIGHTS_NAME = "adapter_model.safetensors"
CONFIG_NAME = "adapter_config.json"
Expand Down
17 changes: 2 additions & 15 deletions src/peft/utils/save_and_load.py
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Expand Up @@ -24,6 +24,7 @@
from packaging import version
from safetensors.torch import load_file as safe_load_file

from .constants import PEFT_TYPE_TO_PREFIX_MAPPING
from .other import (
EMBEDDING_LAYER_NAMES,
SAFETENSORS_WEIGHTS_NAME,
Expand Down Expand Up @@ -357,21 +358,7 @@ def set_peft_model_state_dict(
PeftType.VBLORA,
):
peft_model_state_dict = {}
parameter_prefix = {
PeftType.IA3: "ia3_",
PeftType.LORA: "lora_",
PeftType.ADALORA: "lora_",
PeftType.LOHA: "hada_",
PeftType.LOKR: "lokr_",
PeftType.OFT: "oft_",
PeftType.POLY: "poly_",
PeftType.BOFT: "boft_",
PeftType.LN_TUNING: "ln_tuning_",
PeftType.VERA: "vera_lambda_",
PeftType.FOURIERFT: "fourierft_",
PeftType.HRA: "hra_",
PeftType.VBLORA: "vblora_",
}[config.peft_type]
parameter_prefix = PEFT_TYPE_TO_PREFIX_MAPPING[config.peft_type]
if config.peft_type == PeftType.VBLORA and config.save_only_topk_weights:
num_vectors, _ = model.vblora_vector_bank[adapter_name].shape
state_dict_keys = list(state_dict.keys())
Expand Down
12 changes: 10 additions & 2 deletions tests/testing_common.py
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Expand Up @@ -532,8 +532,16 @@ def _test_load_multiple_adapters(self, model_id, config_cls, config_kwargs):

model = self.transformers_class.from_pretrained(model_id).to(self.torch_device)
model = PeftModel.from_pretrained(model, tmp_dirname, torch_device=self.torch_device)
model.load_adapter(tmp_dirname, adapter_name="other")
model.load_adapter(tmp_dirname, adapter_name="yet-another")

load_result1 = model.load_adapter(tmp_dirname, adapter_name="other")
load_result2 = model.load_adapter(tmp_dirname, adapter_name="yet-another")

# VBLoRA uses a shared "vblora_vector_bank" across all layers, causing it to appear
# in the missing keys list, which leads to failed test cases. So
# skipping the missing keys check for VBLoRA.
if config.peft_type != "VBLORA":
assert load_result1.missing_keys == []
assert load_result2.missing_keys == []

def _test_merge_layers_fp16(self, model_id, config_cls, config_kwargs):
if config_cls not in (LoraConfig, IA3Config, AdaLoraConfig, LoHaConfig, LoKrConfig, VBLoRAConfig):
Expand Down