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hymba_mult
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debug-hf-h
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59047ee6c4 | ||
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c1b920f291 |
@@ -23,7 +23,7 @@ repos:
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hooks:
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- id: flake8
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- repo: https://github.com/PyCQA/pylint
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rev: v2.17.4
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rev: v3.3.0
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hooks:
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- id: pylint
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- repo: https://github.com/pre-commit/mirrors-mypy
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@@ -1,5 +1,5 @@
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[MASTER]
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init-hook="from pylint.config import find_pylintrc; import os, sys; sys.path.append(os.path.dirname(find_pylintrc()))"
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init-hook="from pylint.config import find_default_config_files; import sys; sys.path.append(next(find_default_config_files()).parent.as_posix())"
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[TYPECHECK]
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@@ -12,3 +12,4 @@ generated-members=numpy.*, torch.*
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disable=missing-function-docstring, line-too-long, import-error,
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too-many-arguments, too-many-locals, too-many-statements, too-many-branches, too-few-public-methods,
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too-many-instance-attributes, fixme, import-outside-toplevel, logging-fstring-interpolation,
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too-many-positional-arguments, possibly-used-before-assignment
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23
setup.py
23
setup.py
@@ -1,4 +1,5 @@
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"""setup.py for axolotl"""
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import ast
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import os
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import platform
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@@ -29,15 +30,29 @@ def parse_requirements():
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elif not is_extras and line and line[0] != "#":
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# Handle standard packages
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_install_requires.append(line)
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try:
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xformers_version = [req for req in _install_requires if "xformers" in req][0]
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torchao_version = [req for req in _install_requires if "torchao" in req][0]
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autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
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if "Darwin" in platform.system():
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# don't install xformers on MacOS
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_install_requires.pop(_install_requires.index(xformers_version))
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# skip packages not compatible with OSX
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skip_packages = [
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"bitsandbytes",
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"triton",
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"mamba-ssm",
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"flash-attn",
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"xformers",
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"autoawq",
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"liger-kernel",
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]
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_install_requires = [
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req
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for req in _install_requires
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if re.split(r"[>=<]", req)[0].strip() not in skip_packages
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]
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print(
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_install_requires, [req in skip_packages for req in _install_requires]
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)
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else:
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# detect the version of torch already installed
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# and set it so dependencies don't clobber the torch version
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@@ -43,7 +43,7 @@ def lisa_callback_factory(trainer: "AxolotlTrainer"):
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getattr, self.layers_attribute.split("."), self.trainer.model
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)
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LOG.info(
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f"LISA will activate {self.n_layers}/{len(layers)} layers ({self.n_layers*100/len(layers)}%) every {self.step_interval} steps"
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f"LISA will activate {self.n_layers}/{len(layers)} layers ({self.n_layers * 100 / len(layers)}%) every {self.step_interval} steps"
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)
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def freeze_all_layers(self):
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@@ -270,7 +270,7 @@ def load_sharded_model_quant(
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model.hf_quantizer = AutoHfQuantizer.from_config(quantization_config)
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if cfg.local_rank == 0 and verbose:
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print(f"Loaded model weights in {time.time()-start:.3f} seconds")
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print(f"Loaded model weights in {time.time() - start:.3f} seconds")
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# cleanup any extra memory usage from parallel loading
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torch.cuda.empty_cache()
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@@ -37,7 +37,8 @@ def retry_on_request_exceptions(max_retries=3, delay=1):
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@retry_on_request_exceptions(max_retries=3, delay=5)
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def snapshot_download_w_retry(*args, **kwargs):
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return snapshot_download(*args, **kwargs)
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url = snapshot_download(*args, **kwargs)
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raise f"{args[0]}: {url}"
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@pytest.fixture(scope="session", autouse=True)
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