# Copyright 2022 Cerebras Systems.
#
# 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.
import logging
from typing import Tuple
from cerebras.modelzoo.tools.checkpoint_converters.base_converter import (
BaseCheckpointConverter_HF_CS,
BaseConfigConverter,
BaseConfigConverter_HF_CS,
FormatIndices,
FormatVersions,
)
from cerebras.modelzoo.tools.checkpoint_converters.falcon_7b import (
ConfigConverter_Falcon_7B_HF_CS19,
Converter_Falcon_7B_Headless_HF_CS19,
Converter_Falcon_7B_HF_CS19,
)
from cerebras.modelzoo.tools.checkpoint_converters.falcon_40b import (
ConfigConverter_Falcon_40B_HF_CS20,
Converter_Falcon_40B_Headless_HF_CS20,
Converter_Falcon_40B_HF_CS20,
)
from cerebras.modelzoo.tools.checkpoint_converters.falcon_180b import (
ConfigConverter_Falcon_180B_HF_CS20,
ConfigConverter_Falcon_180B_HF_CS21,
Converter_Falcon_180B_Headless_HF_CS20,
Converter_Falcon_180B_Headless_HF_CS21,
Converter_Falcon_180B_HF_CS20,
Converter_Falcon_180B_HF_CS21,
)
from cerebras.modelzoo.tools.checkpoint_converters.gptj_hf_cs import (
Converter_GPTJ_LMHeadModel_CS20_CS21,
)
[docs]class Converter_Falcon_Headless_HF_CS20(BaseCheckpointConverter_HF_CS):
config2model_subconverters = {
ConfigConverter_Falcon_7B_HF_CS19: Converter_Falcon_7B_Headless_HF_CS19,
ConfigConverter_Falcon_40B_HF_CS20: Converter_Falcon_40B_Headless_HF_CS20,
ConfigConverter_Falcon_180B_HF_CS20: Converter_Falcon_180B_Headless_HF_CS20,
}
def __init__(self):
super().__init__()
self.rules = []
@classmethod
def select_subconverter(
cls,
config,
from_index: int,
**kwargs,
):
config_subconverter = (
cls.get_config_converter_class().select_subconverter(
config, from_index
)
)
return cls.config2model_subconverters[config_subconverter]
@classmethod
def convert(cls, checkpoint, configs, converter_indices, **kwargs):
subconverter = cls.select_subconverter(
configs[converter_indices.direction], converter_indices.direction
)
instance = subconverter()
new_checkpoint = instance.convert_helper(
checkpoint, configs, converter_indices, **kwargs
)
return new_checkpoint
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.9", "cs-2.0"))
@classmethod
def converter_note(cls) -> str:
return (
"{} FalconModel or RWModel <-> {} GPTJModel (configured as Falcon)\n"
"The HF model doesn't contain a language model head while the CS "
"one does. When converting to CS, the exported checkpoint will "
"contain a language model head initialized to default random "
"values. When converting to HF, the language model head will be "
"dropped."
).format(cls.formats()[0], cls.formats()[1])
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Falcon_HF_CS20
[docs]class Converter_Falcon_HF_CS20(BaseCheckpointConverter_HF_CS):
config2model_subconverters = {
ConfigConverter_Falcon_7B_HF_CS19: Converter_Falcon_7B_HF_CS19,
ConfigConverter_Falcon_40B_HF_CS20: Converter_Falcon_40B_HF_CS20,
ConfigConverter_Falcon_180B_HF_CS20: Converter_Falcon_180B_HF_CS20,
}
def __init__(self):
super().__init__()
self.rules = []
@classmethod
def select_subconverter(
cls,
config,
from_index: int,
**kwargs,
):
config_subconverter = (
cls.get_config_converter_class().select_subconverter(
config, from_index
)
)
return cls.config2model_subconverters[config_subconverter]
@classmethod
def convert(cls, checkpoint, configs, converter_indices, **kwargs):
subconverter = cls.select_subconverter(
configs[converter_indices.direction], converter_indices.direction
)
instance = subconverter()
new_checkpoint = instance.convert_helper(
checkpoint, configs, converter_indices, **kwargs
)
return new_checkpoint
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.9", "cs-2.0"))
@classmethod
def converter_note(cls) -> str:
return (
f"{cls.formats()[0]} FalconForCausalLM or RWForCausalLM <-> {cls.formats()[1]} "
f"GPTJModel (configured as Falcon) with LM head. When converting "
f"to HF, make sure that transformers>=4.41.0 to have the most "
f"recent falcon implementation."
)
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Falcon_HF_CS20
[docs]class ConfigConverter_Falcon_HF_CS20(BaseConfigConverter_HF_CS):
def __init__(self):
super().__init__()
self.rules = []
@classmethod
def select_subconverter(
cls,
config,
from_index: int,
**kwargs,
):
logging.info("HF's Falcon 7B, 40B, and 180B use different codebases.")
if from_index == 0:
if config.get("model_type", "") == "falcon":
logging.info(
"The model that you're using was generated using the 180B "
"style codebase (model=FalconModel)"
)
return ConfigConverter_Falcon_180B_HF_CS20
elif "n_head_kv" not in config: # MQA, 7b structure
logging.info(
"The model that you're using was generated using the 7B "
"style codebase (model=RefinedWeb) which only supports "
"multi-query attention (not grouped query)."
)
return ConfigConverter_Falcon_7B_HF_CS19
else: # GQA, 40B structure
logging.info(
"The model that you're using was generated using the 40B "
"style codebase (model=RefinedWeb) with grouped query "
"attention support"
)
return ConfigConverter_Falcon_40B_HF_CS20
else:
logging.info(
"The output will be formatted for the official 180B style "
"codebase (model=FalconModel) rather than the 7B or 40B style "
"codebases (model=RefinedWeb)"
)
return ConfigConverter_Falcon_180B_HF_CS20
@classmethod
def convert(
cls,
config,
converter_indices: FormatIndices,
drop_unmatched_keys: bool = False,
no_progress_bar: bool = True,
debug: bool = False,
):
subconverter = cls.select_subconverter(
config, converter_indices.direction
)
instance = subconverter()
return instance.convert_helper(
config,
converter_indices,
drop_unmatched_keys=drop_unmatched_keys,
no_progress_bar=no_progress_bar,
debug=debug,
)
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.9", "cs-2.0"))
###########################################################
# In CS 2.1, we refactored the embedding layer.
# CS 2.0 <> CS 2.1, and HF <> CS 2.1 converters:
###########################################################
[docs]class Converter_Falcon_CS20_CS21(Converter_GPTJ_LMHeadModel_CS20_CS21):
def __init__(self):
super().__init__()
@classmethod
def converter_note(cls) -> str:
return "GPT2LMHeadModel class (configured as falcon)"
[docs]class ConfigConverter_Falcon_HF_CS21(ConfigConverter_Falcon_HF_CS20):
@classmethod
def select_subconverter(
cls,
config,
from_index: int,
**kwargs,
):
sub_converter = super().select_subconverter(
config, from_index, **kwargs
)
# Only CS21 is different because others don't support alibi
if sub_converter == ConfigConverter_Falcon_180B_HF_CS20:
sub_converter = ConfigConverter_Falcon_180B_HF_CS21
return sub_converter
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (
FormatVersions("hf"),
FormatVersions("cs-2.1", "cs-2.2", "cs-2.3"),
)
[docs]class Converter_Falcon_Headless_HF_CS21(Converter_Falcon_Headless_HF_CS20):
config2model_subconverters = {
ConfigConverter_Falcon_7B_HF_CS19: Converter_Falcon_7B_Headless_HF_CS19,
ConfigConverter_Falcon_40B_HF_CS20: Converter_Falcon_40B_Headless_HF_CS20,
ConfigConverter_Falcon_180B_HF_CS21: Converter_Falcon_180B_Headless_HF_CS21,
}
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (
FormatVersions("hf"),
FormatVersions("cs-2.1", "cs-2.2", "cs-2.3"),
)
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Falcon_HF_CS21
[docs]class Converter_Falcon_HF_CS21(Converter_Falcon_HF_CS20):
config2model_subconverters = {
ConfigConverter_Falcon_7B_HF_CS19: Converter_Falcon_7B_HF_CS19,
ConfigConverter_Falcon_40B_HF_CS20: Converter_Falcon_40B_HF_CS20,
ConfigConverter_Falcon_180B_HF_CS21: Converter_Falcon_180B_HF_CS21,
}
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (
FormatVersions("hf"),
FormatVersions("cs-2.1", "cs-2.2", "cs-2.3"),
)
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Falcon_HF_CS21