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fused_ops_and_kernels.py
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fused_ops_and_kernels.py
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# Copyright The FMS HF Tuning Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Standard
from dataclasses import dataclass
from typing import List
# Local
from .utils import ensure_nested_dataclasses_initialized, parsable_dataclass
@parsable_dataclass
@dataclass
class FusedLoraConfig(List):
# load unsloth optimizations for these 4bit base layer weights.
# currently only support "auto_gptq" and "bitsandbytes"
base_layer: str = None
# fused kernels for lora linear layers
fused_lora: bool = False
def __post_init__(self):
if self.base_layer is not None and self.base_layer not in {
"auto_gptq",
"bitsandbytes",
}:
raise ValueError(f"base_layer set to invalid value '{self.base_layer}'")
if self.base_layer is not None and not self.fused_lora:
raise ValueError(
f"base_layer set to '{self.base_layer}' so fused_lora must be set to True"
)
@parsable_dataclass
@dataclass
class FastKernelsConfig(List):
# fast loss triton kernels
fast_loss: bool = False
# fast rms norm triton kernels
fast_rms_layernorm: bool = False
# fast RoPE embedding triton kernels
fast_rope_embeddings: bool = False
@dataclass
class FusedOpsAndKernelsConfig:
# fused lora ops
fused_lora: FusedLoraConfig = None
# fast kernels
fast_kernels: FastKernelsConfig = None
def __post_init__(self):
# ensure nested dataclasses initialized
ensure_nested_dataclasses_initialized(self)