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remove deprecated extra metadata kwarg from pydantic Field (#2081) [skip ci]

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Wing Lian
2024-11-19 10:30:10 -05:00
committed by GitHub
parent a77c8a71cf
commit ec59d4cb83
1 changed files with 31 additions and 24 deletions
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55
src/axolotl/utils/config/models/input/v0_4_1/__init__.py
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@@ -250,8 +250,10 @@ class KTODataset(BaseModel):
class LoftQConfig(BaseModel):
"""LoftQ configuration subset"""
loftq_bits: int = Field(default=4, metadata={"help": "Quantization bits for LoftQ"})
# loftq_iter: int = Field(default=1, metadata={"help": "Alternating iterations for LoftQ"})
loftq_bits: int = Field(
default=4, json_schema_extra={"description": "Quantization bits for LoftQ"}
)
# loftq_iter: int = Field(default=1, json_schema_extra={"description": "Alternating iterations for LoftQ"})
class PeftConfig(BaseModel):
@@ -294,8 +296,8 @@ class LoraConfig(BaseModel):
qlora_sharded_model_loading: Optional[bool] = Field(
default=False,
metadata={
"help": "load qlora model in sharded format for FSDP using answer.ai technique."
json_schema_extra={
"description": "load qlora model in sharded format for FSDP using answer.ai technique."
},
)
lora_on_cpu: Optional[bool] = None
@@ -304,13 +306,15 @@ class LoraConfig(BaseModel):
loraplus_lr_ratio: Optional[float] = Field(
default=None,
metadata={
"help": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
json_schema_extra={
"description": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
},
)
loraplus_lr_embedding: Optional[float] = Field(
default=1e-6,
metadata={"help": "loraplus learning rate for lora embedding layers."},
json_schema_extra={
"description": "loraplus learning rate for lora embedding layers."
},
)
merge_lora: Optional[bool] = None
@@ -380,10 +384,10 @@ class ModelInputConfig(BaseModel):
tokenizer_use_fast: Optional[bool] = None
tokenizer_legacy: Optional[bool] = None
tokenizer_type: Optional[str] = Field(
default=None, metadata={"help": "transformers tokenizer class"}
default=None, json_schema_extra={"description": "transformers tokenizer class"}
)
processor_type: Optional[str] = Field(
default=None, metadata={"help": "transformers processor class"}
default=None, json_schema_extra={"description": "transformers processor class"}
)
trust_remote_code: Optional[bool] = None
@@ -405,18 +409,18 @@ class HyperparametersConfig(BaseModel):
gradient_accumulation_steps: Optional[int] = Field(default=1)
micro_batch_size: Optional[int] = Field(
default=1,
metadata={"help": "per gpu micro batch size for training"},
json_schema_extra={"description": "per gpu micro batch size for training"},
)
batch_size: Optional[int] = Field(
default=None,
metadata={
"help": "Total batch size, we do not recommended setting this manually"
json_schema_extra={
"description": "Total batch size, we do not recommended setting this manually"
},
)
eval_batch_size: Optional[int] = Field(
default=None,
metadata={
"help": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
json_schema_extra={
"description": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
},
)
@@ -441,12 +445,13 @@ class HyperparametersConfig(BaseModel):
]
] = OptimizerNames.ADAMW_HF.value
optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
default=None, metadata={"help": "Optional arguments to supply to optimizer."}
default=None,
json_schema_extra={"description": "Optional arguments to supply to optimizer."},
)
optim_target_modules: Optional[Union[List[str], Literal["all_linear"]]] = Field(
default=None,
metadata={
"help": "The target modules to optimize, i.e. the module names that you would like to train."
json_schema_extra={
"description": "The target modules to optimize, i.e. the module names that you would like to train."
},
)
torchdistx_path: Optional[str] = None
@@ -506,15 +511,15 @@ class LISAConfig(BaseModel):
lisa_n_layers: Optional[int] = Field(
default=None,
metadata={"help": "the number of activate layers in LISA"},
json_schema_extra={"description": "the number of activate layers in LISA"},
)
lisa_step_interval: Optional[int] = Field(
default=None,
metadata={"help": "how often to switch layers in LISA"},
json_schema_extra={"description": "how often to switch layers in LISA"},
)
lisa_layers_attribute: Optional[str] = Field(
default="model.layers",
metadata={"help": "path under the model to access the layers"},
json_schema_extra={"description": "path under the model to access the layers"},
)
@@ -613,7 +618,8 @@ class AxolotlInputConfig(
pretraining_dataset: Optional[ # type: ignore
conlist(Union[PretrainingDataset, SFTDataset], min_length=1)
] = Field(
default=None, metadata={"help": {"streaming dataset to use for pretraining"}}
default=None,
json_schema_extra={"description": "streaming dataset to use for pretraining"},
)
dataset_processes: Optional[int] = Field(default=os.cpu_count())
dataset_keep_in_memory: Optional[bool] = None
@@ -673,7 +679,8 @@ class AxolotlInputConfig(
sequence_len: int = Field(default=512)
min_sample_len: Optional[int] = None
max_prompt_len: int = Field(
default=512, metadata={"help": "maximum prompt length for RL training"}
default=512,
json_schema_extra={"description": "maximum prompt length for RL training"},
)
sample_packing: Optional[bool] = None
sample_packing_group_size: Optional[int] = 100_000
@@ -692,8 +699,8 @@ class AxolotlInputConfig(
pretrain_multipack_buffer_size: Optional[int] = 10_000
pretrain_multipack_attn: Optional[bool] = Field(
default=True,
metadata={
"help": "whether to prevent cross attention for packed sequences during pretraining",
json_schema_extra={
"description": "whether to prevent cross attention for packed sequences during pretraining",
},
)