danidarko/docs · Datasets at Hugging Face

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import lm_eval.models.utils
from lm_eval.api.registry import register_model
from lm_eval.models.huggingface import HFLM

@register_model('mamba_ssm')
class MambaLMWrapper(HFLM):

def __init__(self, pretrained='state-spaces/mamba-130m', **kwargs) -> None:
if 'backend' in kwargs:
assert kwargs['backend'] == 'causal'
super().__init__(pretrained=pretrained, backend=kwargs.pop('backend', 'causal'), tokenizer=kwargs.pop('tokenizer', 'EleutherAI/gpt-neox-20b'), max_length=kwargs.pop('max_length', 2048), **kwargs)

def _get_config(self, pretrained: str, **kwargs) -> None:
try:
from mamba_ssm.utils.hf import load_config_hf
except ModuleNotFoundError:
raise Exception("attempted to use 'mamba_ssm' LM type, but package `mamba_ssm` is not installed. please install mamba via `pip install lm-eval[mamba]` or `pip install -e .[mamba]`")
self._config = load_config_hf(pretrained)

def _create_model(self, pretrained: str, dtype: Optional[Union[str, torch.dtype]]='float16', **kwargs) -> None:
try:
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
except ModuleNotFoundError:
raise Exception("attempted to use 'mamba_ssm' LM type, but package `mamba_ssm` is not installed. please install mamba via `pip install lm-eval[mamba]` or `pip install -e .[mamba]`")
self._model = MambaLMHeadModel.from_pretrained(pretrained, device=self._device, dtype=torch.float16 if dtype == 'auto' else lm_eval.models.utils.get_dtype(dtype))

def _model_generate(self, context, max_length, stop, **generation_kwargs):
for key in ('do_sample', 'attention_mask'):
if key in generation_kwargs:
generation_kwargs.pop(key)
return self.model.generate(input_ids=context, max_length=max_length, **generation_kwargs)

# File: lm-evaluation-harness-main/lm_eval/models/nemo_lm.py
import importlib
import pathlib
from copy import deepcopy
from typing import List, Literal
import filelock
import numpy as np
import torch
from tqdm import tqdm
from lm_eval.api.instance import Instance
from lm_eval.api.model import LM
from lm_eval.api.registry import register_model
from lm_eval.models.utils import Collator
from lm_eval.utils import eval_logger, get_rolling_token_windows, make_disjoint_window, simple_parse_args_string

def _patch_pretrained_cfg(pretrained_cfg, trainer, tensor_model_parallel_size, pipeline_model_parallel_size):
try:
import omegaconf
except ModuleNotFoundError:
raise Exception("Attempted to use 'nemo_lm' model type, but package `nemo` is not installedPlease install nemo following the instructions in the README: either with a NVIDIA PyTorch or NeMo container, or installing nemo following https://github.com/NVIDIA/NeMo.")
omegaconf.OmegaConf.set_struct(pretrained_cfg, True)
with omegaconf.open_dict(pretrained_cfg):
attributes_to_update = {'sequence_parallel': False, 'activations_checkpoint_granularity': None, 'activations_checkpoint_method': None, 'precision': trainer.precision, 'global_batch_size': None, 'tensor_model_parallel_size': tensor_model_parallel_size, 'pipeline_model_parallel_size': pipeline_model_parallel_size, 'apply_rope_fusion': False}
for (name, value) in attributes_to_update.items():
if hasattr(pretrained_cfg, name):
pretrained_cfg[name] = value
return pretrained_cfg

def _get_target_from_class(target_class) -> str:
return f'{target_class.__module__}.{target_class.__name__}'

def load_model(model_path: str, trainer, tensor_model_parallel_size: int, pipeline_model_parallel_size: int) -> torch.nn.Module:
try:
from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
except ModuleNotFoundError:
raise Exception("Attempted to use 'nemo_lm' model type, but package `nemo` is not installedPlease install nemo following the instructions in the README: either with a NVIDIA PyTorch or NeMo container, or installing nemo following https://github.com/NVIDIA/NeMo.")
model_path = pathlib.Path(model_path)
save_restore_connector = NLPSaveRestoreConnector()
if model_path.is_dir():
save_restore_connector.model_extracted_dir = model_path.as_posix()
pretrained_cfg = save_restore_connector.restore_from(None, model_path.as_posix(), return_config=True, trainer=trainer)
if not hasattr(pretrained_cfg, 'target'):
pretrained_cfg['target'] = _get_target_from_class(MegatronGPTModel)
pretrained_cfg = _patch_pretrained_cfg(pretrained_cfg, trainer, tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=pipeline_model_parallel_size)
model_to_load_path = model_path
override_config = pretrained_cfg
(module_name, class_name) = override_config.target.rsplit('.', 1)
model_class = getattr(importlib.import_module(module_name), class_name)
tokenizer_lock = filelock.FileLock(f'/tmp/{model_path.name}.tokenizer.lock')

def _synced_build_tokenizer(self):
with tokenizer_lock:
self._original_build_tokenizer()
model_class._original_build_tokenizer = model_class._build_tokenizer
model_class._build_tokenizer = _synced_build_tokenizer
model = model_class.restore_from(restore_path=model_to_load_path.as_posix(), trainer=trainer, override_config_path=override_config, save_restore_connector=save_restore_connector, map_location=f'cuda:{trainer.local_rank}')
model.freeze()
model.training = False
try:
model.model.language_model.encoder.activations_checkpoint_method = None
except AttributeError:
pass
return model

def setup_distributed_environment(trainer):
try:
from nemo.utils.app_state import AppState

import lm_eval.models.utils

from lm_eval.api.registry import register_model

from lm_eval.models.huggingface import HFLM

@register_model('mamba_ssm')

class MambaLMWrapper(HFLM):

def __init__(self, pretrained='state-spaces/mamba-130m', **kwargs) -> None:

if 'backend' in kwargs:

assert kwargs['backend'] == 'causal'

super().__init__(pretrained=pretrained, backend=kwargs.pop('backend', 'causal'), tokenizer=kwargs.pop('tokenizer', 'EleutherAI/gpt-neox-20b'), max_length=kwargs.pop('max_length', 2048), **kwargs)

def _get_config(self, pretrained: str, **kwargs) -> None:

try:

from mamba_ssm.utils.hf import load_config_hf

except ModuleNotFoundError:

raise Exception("attempted to use 'mamba_ssm' LM type, but package `mamba_ssm` is not installed. please install mamba via `pip install lm-eval[mamba]` or `pip install -e .[mamba]`")

self._config = load_config_hf(pretrained)

def _create_model(self, pretrained: str, dtype: Optional[Union[str, torch.dtype]]='float16', **kwargs) -> None:

try:

from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel

except ModuleNotFoundError:

raise Exception("attempted to use 'mamba_ssm' LM type, but package `mamba_ssm` is not installed. please install mamba via `pip install lm-eval[mamba]` or `pip install -e .[mamba]`")

self._model = MambaLMHeadModel.from_pretrained(pretrained, device=self._device, dtype=torch.float16 if dtype == 'auto' else lm_eval.models.utils.get_dtype(dtype))

def _model_generate(self, context, max_length, stop, **generation_kwargs):

for key in ('do_sample', 'attention_mask'):

if key in generation_kwargs:

generation_kwargs.pop(key)

return self.model.generate(input_ids=context, max_length=max_length, **generation_kwargs)

# File: lm-evaluation-harness-main/lm_eval/models/nemo_lm.py

import importlib

import pathlib

from copy import deepcopy

from typing import List, Literal

import filelock

import numpy as np

import torch

from tqdm import tqdm

from lm_eval.api.instance import Instance

from lm_eval.api.model import LM

from lm_eval.api.registry import register_model

from lm_eval.models.utils import Collator

from lm_eval.utils import eval_logger, get_rolling_token_windows, make_disjoint_window, simple_parse_args_string

def _patch_pretrained_cfg(pretrained_cfg, trainer, tensor_model_parallel_size, pipeline_model_parallel_size):

try:

import omegaconf

except ModuleNotFoundError:

raise Exception("Attempted to use 'nemo_lm' model type, but package `nemo` is not installedPlease install nemo following the instructions in the README: either with a NVIDIA PyTorch or NeMo container, or installing nemo following https://github.com/NVIDIA/NeMo.")

omegaconf.OmegaConf.set_struct(pretrained_cfg, True)

with omegaconf.open_dict(pretrained_cfg):

attributes_to_update = {'sequence_parallel': False, 'activations_checkpoint_granularity': None, 'activations_checkpoint_method': None, 'precision': trainer.precision, 'global_batch_size': None, 'tensor_model_parallel_size': tensor_model_parallel_size, 'pipeline_model_parallel_size': pipeline_model_parallel_size, 'apply_rope_fusion': False}

for (name, value) in attributes_to_update.items():

if hasattr(pretrained_cfg, name):

pretrained_cfg[name] = value

return pretrained_cfg

def _get_target_from_class(target_class) -> str:

return f'{target_class.__module__}.{target_class.__name__}'

def load_model(model_path: str, trainer, tensor_model_parallel_size: int, pipeline_model_parallel_size: int) -> torch.nn.Module:

try:

from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel

from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector

except ModuleNotFoundError:

raise Exception("Attempted to use 'nemo_lm' model type, but package `nemo` is not installedPlease install nemo following the instructions in the README: either with a NVIDIA PyTorch or NeMo container, or installing nemo following https://github.com/NVIDIA/NeMo.")

model_path = pathlib.Path(model_path)

save_restore_connector = NLPSaveRestoreConnector()

if model_path.is_dir():

save_restore_connector.model_extracted_dir = model_path.as_posix()

pretrained_cfg = save_restore_connector.restore_from(None, model_path.as_posix(), return_config=True, trainer=trainer)

if not hasattr(pretrained_cfg, 'target'):

pretrained_cfg['target'] = _get_target_from_class(MegatronGPTModel)

pretrained_cfg = _patch_pretrained_cfg(pretrained_cfg, trainer, tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=pipeline_model_parallel_size)

model_to_load_path = model_path

override_config = pretrained_cfg

(module_name, class_name) = override_config.target.rsplit('.', 1)

model_class = getattr(importlib.import_module(module_name), class_name)

tokenizer_lock = filelock.FileLock(f'/tmp/{model_path.name}.tokenizer.lock')

def _synced_build_tokenizer(self):

with tokenizer_lock:

self._original_build_tokenizer()

model_class._original_build_tokenizer = model_class._build_tokenizer

model_class._build_tokenizer = _synced_build_tokenizer

model = model_class.restore_from(restore_path=model_to_load_path.as_posix(), trainer=trainer, override_config_path=override_config, save_restore_connector=save_restore_connector, map_location=f'cuda:{trainer.local_rank}')

model.freeze()

model.training = False

try:

model.model.language_model.encoder.activations_checkpoint_method = None

except AttributeError:

pass

return model

def setup_distributed_environment(trainer):

try:

from nemo.utils.app_state import AppState