# Copyright 2022 The HuggingFace Team. All rights reserved. # # 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 utilities: Utilities related to imports and our lazy inits. """ import importlib.machinery import importlib.metadata import importlib.util import json import os import shutil import subprocess import sys import warnings from collections import OrderedDict from functools import lru_cache from itertools import chain from types import ModuleType from typing import Any, Dict, FrozenSet, Optional, Set, Tuple, Union from packaging import version from . import logging logger = logging.get_logger(__name__) # pylint: disable=invalid-name # TODO: This doesn't work for all packages (`bs4`, `faiss`, etc.) Talk to Sylvain to see how to do with it better. def _is_package_available(pkg_name: str, return_version: bool = False) -> Union[Tuple[bool, str], bool]: # Check if the package spec exists and grab its version to avoid importing a local directory package_exists = importlib.util.find_spec(pkg_name) is not None package_version = "N/A" if package_exists: try: # Primary method to get the package version package_version = importlib.metadata.version(pkg_name) except importlib.metadata.PackageNotFoundError: # Fallback method: Only for "torch" and versions containing "dev" if pkg_name == "torch": try: package = importlib.import_module(pkg_name) temp_version = getattr(package, "__version__", "N/A") # Check if the version contains "dev" if "dev" in temp_version: package_version = temp_version package_exists = True else: package_exists = False except ImportError: # If the package can't be imported, it's not available package_exists = False else: # For packages other than "torch", don't attempt the fallback and set as not available package_exists = False logger.debug(f"Detected {pkg_name} version: {package_version}") if return_version: return package_exists, package_version else: return package_exists ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"} ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"}) USE_TF = os.environ.get("USE_TF", "AUTO").upper() USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper() USE_JAX = os.environ.get("USE_FLAX", "AUTO").upper() # Try to run a native pytorch job in an environment with TorchXLA installed by setting this value to 0. USE_TORCH_XLA = os.environ.get("USE_TORCH_XLA", "1").upper() FORCE_TF_AVAILABLE = os.environ.get("FORCE_TF_AVAILABLE", "AUTO").upper() # `transformers` requires `torch>=1.11` but this variable is exposed publicly, and we can't simply remove it. # This is the version of torch required to run torch.fx features and torch.onnx with dictionary inputs. TORCH_FX_REQUIRED_VERSION = version.parse("1.10") ACCELERATE_MIN_VERSION = "0.26.0" FSDP_MIN_VERSION = "1.12.0" GGUF_MIN_VERSION = "0.10.0" XLA_FSDPV2_MIN_VERSION = "2.2.0" HQQ_MIN_VERSION = "0.2.1" _accelerate_available, _accelerate_version = _is_package_available("accelerate", return_version=True) _apex_available = _is_package_available("apex") _aqlm_available = _is_package_available("aqlm") _av_available = importlib.util.find_spec("av") is not None _bitsandbytes_available = _is_package_available("bitsandbytes") _eetq_available = _is_package_available("eetq") _fbgemm_gpu_available = _is_package_available("fbgemm_gpu") _galore_torch_available = _is_package_available("galore_torch") _lomo_available = _is_package_available("lomo_optim") _grokadamw_available = _is_package_available("grokadamw") _schedulefree_available = _is_package_available("schedulefree") # `importlib.metadata.version` doesn't work with `bs4` but `beautifulsoup4`. For `importlib.util.find_spec`, reversed. _bs4_available = importlib.util.find_spec("bs4") is not None _coloredlogs_available = _is_package_available("coloredlogs") # `importlib.metadata.util` doesn't work with `opencv-python-headless`. _cv2_available = importlib.util.find_spec("cv2") is not None _datasets_available = _is_package_available("datasets") _detectron2_available = _is_package_available("detectron2") # We need to check both `faiss` and `faiss-cpu`. _faiss_available = importlib.util.find_spec("faiss") is not None try: _faiss_version = importlib.metadata.version("faiss") logger.debug(f"Successfully imported faiss version {_faiss_version}") except importlib.metadata.PackageNotFoundError: try: _faiss_version = importlib.metadata.version("faiss-cpu") logger.debug(f"Successfully imported faiss version {_faiss_version}") except importlib.metadata.PackageNotFoundError: _faiss_available = False _ftfy_available = _is_package_available("ftfy") _g2p_en_available = _is_package_available("g2p_en") _ipex_available, _ipex_version = _is_package_available("intel_extension_for_pytorch", return_version=True) _jieba_available = _is_package_available("jieba") _jinja_available = _is_package_available("jinja2") _kenlm_available = _is_package_available("kenlm") _keras_nlp_available = _is_package_available("keras_nlp") _levenshtein_available = _is_package_available("Levenshtein") _librosa_available = _is_package_available("librosa") _natten_available = _is_package_available("natten") _nltk_available = _is_package_available("nltk") _onnx_available = _is_package_available("onnx") _openai_available = _is_package_available("openai") _optimum_available = _is_package_available("optimum") _auto_gptq_available = _is_package_available("auto_gptq") # `importlib.metadata.version` doesn't work with `awq` _auto_awq_available = importlib.util.find_spec("awq") is not None _quanto_available = _is_package_available("quanto") _is_optimum_quanto_available = False try: importlib.metadata.version("optimum_quanto") _is_optimum_quanto_available = True except importlib.metadata.PackageNotFoundError: _is_optimum_quanto_available = False # For compressed_tensors, only check spec to allow compressed_tensors-nightly package _compressed_tensors_available = importlib.util.find_spec("compressed_tensors") is not None _pandas_available = _is_package_available("pandas") _peft_available = _is_package_available("peft") _phonemizer_available = _is_package_available("phonemizer") _uroman_available = _is_package_available("uroman") _psutil_available = _is_package_available("psutil") _py3nvml_available = _is_package_available("py3nvml") _pyctcdecode_available = _is_package_available("pyctcdecode") _pygments_available = _is_package_available("pygments") _pytesseract_available = _is_package_available("pytesseract") _pytest_available = _is_package_available("pytest") _pytorch_quantization_available = _is_package_available("pytorch_quantization") _rjieba_available = _is_package_available("rjieba") _sacremoses_available = _is_package_available("sacremoses") _safetensors_available = _is_package_available("safetensors") _scipy_available = _is_package_available("scipy") _sentencepiece_available = _is_package_available("sentencepiece") _is_seqio_available = _is_package_available("seqio") _is_gguf_available, _gguf_version = _is_package_available("gguf", return_version=True) _sklearn_available = importlib.util.find_spec("sklearn") is not None if _sklearn_available: try: importlib.metadata.version("scikit-learn") except importlib.metadata.PackageNotFoundError: _sklearn_available = False _smdistributed_available = importlib.util.find_spec("smdistributed") is not None _soundfile_available = _is_package_available("soundfile") _spacy_available = _is_package_available("spacy") _sudachipy_available, _sudachipy_version = _is_package_available("sudachipy", return_version=True) _tensorflow_probability_available = _is_package_available("tensorflow_probability") _tensorflow_text_available = _is_package_available("tensorflow_text") _tf2onnx_available = _is_package_available("tf2onnx") _timm_available = _is_package_available("timm") _tokenizers_available = _is_package_available("tokenizers") _torchaudio_available = _is_package_available("torchaudio") _torchao_available = _is_package_available("torchao") _torchdistx_available = _is_package_available("torchdistx") _torchvision_available, _torchvision_version = _is_package_available("torchvision", return_version=True) _mlx_available = _is_package_available("mlx") _hqq_available, _hqq_version = _is_package_available("hqq", return_version=True) _tiktoken_available = _is_package_available("tiktoken") _blobfile_available = _is_package_available("blobfile") _liger_kernel_available = _is_package_available("liger_kernel") _torch_version = "N/A" _torch_available = False if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES: _torch_available, _torch_version = _is_package_available("torch", return_version=True) else: logger.info("Disabling PyTorch because USE_TF is set") _torch_available = False _tf_version = "N/A" _tf_available = False if FORCE_TF_AVAILABLE in ENV_VARS_TRUE_VALUES: _tf_available = True else: if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES: # Note: _is_package_available("tensorflow") fails for tensorflow-cpu. Please test any changes to the line below # with tensorflow-cpu to make sure it still works! _tf_available = importlib.util.find_spec("tensorflow") is not None if _tf_available: candidates = ( "tensorflow", "tensorflow-cpu", "tensorflow-gpu", "tf-nightly", "tf-nightly-cpu", "tf-nightly-gpu", "tf-nightly-rocm", "intel-tensorflow", "intel-tensorflow-avx512", "tensorflow-rocm", "tensorflow-macos", "tensorflow-aarch64", ) _tf_version = None # For the metadata, we have to look for both tensorflow and tensorflow-cpu for pkg in candidates: try: _tf_version = importlib.metadata.version(pkg) break except importlib.metadata.PackageNotFoundError: pass _tf_available = _tf_version is not None if _tf_available: if version.parse(_tf_version) < version.parse("2"): logger.info( f"TensorFlow found but with version {_tf_version}. Transformers requires version 2 minimum." ) _tf_available = False else: logger.info("Disabling Tensorflow because USE_TORCH is set") _essentia_available = importlib.util.find_spec("essentia") is not None try: _essentia_version = importlib.metadata.version("essentia") logger.debug(f"Successfully imported essentia version {_essentia_version}") except importlib.metadata.PackageNotFoundError: _essentia_version = False _pretty_midi_available = importlib.util.find_spec("pretty_midi") is not None try: _pretty_midi_version = importlib.metadata.version("pretty_midi") logger.debug(f"Successfully imported pretty_midi version {_pretty_midi_version}") except importlib.metadata.PackageNotFoundError: _pretty_midi_available = False ccl_version = "N/A" _is_ccl_available = ( importlib.util.find_spec("torch_ccl") is not None or importlib.util.find_spec("oneccl_bindings_for_pytorch") is not None ) try: ccl_version = importlib.metadata.version("oneccl_bind_pt") logger.debug(f"Detected oneccl_bind_pt version {ccl_version}") except importlib.metadata.PackageNotFoundError: _is_ccl_available = False _flax_available = False if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES: _flax_available, _flax_version = _is_package_available("flax", return_version=True) if _flax_available: _jax_available, _jax_version = _is_package_available("jax", return_version=True) if _jax_available: logger.info(f"JAX version {_jax_version}, Flax version {_flax_version} available.") else: _flax_available = _jax_available = False _jax_version = _flax_version = "N/A" _torch_fx_available = False if _torch_available: torch_version = version.parse(_torch_version) _torch_fx_available = (torch_version.major, torch_version.minor) >= ( TORCH_FX_REQUIRED_VERSION.major, TORCH_FX_REQUIRED_VERSION.minor, ) _torch_xla_available = False if USE_TORCH_XLA in ENV_VARS_TRUE_VALUES: _torch_xla_available, _torch_xla_version = _is_package_available("torch_xla", return_version=True) if _torch_xla_available: logger.info(f"Torch XLA version {_torch_xla_version} available.") def is_kenlm_available(): return _kenlm_available def is_cv2_available(): return _cv2_available def is_torch_available(): return _torch_available def is_accelerate_available(min_version: str = ACCELERATE_MIN_VERSION): return _accelerate_available and version.parse(_accelerate_version) >= version.parse(min_version) def is_torch_deterministic(): """ Check whether pytorch uses deterministic algorithms by looking if torch.set_deterministic_debug_mode() is set to 1 or 2" """ import torch if torch.get_deterministic_debug_mode() == 0: return False else: return True def is_hqq_available(min_version: str = HQQ_MIN_VERSION): return _hqq_available and version.parse(_hqq_version) >= version.parse(min_version) def is_pygments_available(): return _pygments_available def get_torch_version(): return _torch_version def is_torch_sdpa_available(): if not is_torch_available(): return False elif _torch_version == "N/A": return False # NOTE: We require torch>=2.1 (and not torch>=2.0) to use SDPA in Transformers for two reasons: # - Allow the global use of the `scale` argument introduced in https://github.com/pytorch/pytorch/pull/95259 # - Memory-efficient attention supports arbitrary attention_mask: https://github.com/pytorch/pytorch/pull/104310 # NOTE: MLU is OK with non-contiguous inputs. if is_torch_mlu_available(): return version.parse(_torch_version) >= version.parse("2.1.0") # NOTE: We require torch>=2.1.1 to avoid a numerical issue in SDPA with non-contiguous inputs: https://github.com/pytorch/pytorch/issues/112577 return version.parse(_torch_version) >= version.parse("2.1.1") def is_torch_flex_attn_available(): if not is_torch_available(): return False elif _torch_version == "N/A": return False # TODO check if some bugs cause push backs on the exact version # NOTE: We require torch>=2.5.0 as it is the first release return version.parse(_torch_version) >= version.parse("2.5.0") def is_torchvision_available(): return _torchvision_available def is_torchvision_v2_available(): if not is_torchvision_available(): return False # NOTE: We require torchvision>=0.15 as v2 transforms are available from this version: https://pytorch.org/vision/stable/transforms.html#v1-or-v2-which-one-should-i-use return version.parse(_torchvision_version) >= version.parse("0.15") def is_galore_torch_available(): return _galore_torch_available def is_lomo_available(): return _lomo_available def is_grokadamw_available(): return _grokadamw_available def is_schedulefree_available(): return _schedulefree_available def is_pyctcdecode_available(): return _pyctcdecode_available def is_librosa_available(): return _librosa_available def is_essentia_available(): return _essentia_available def is_pretty_midi_available(): return _pretty_midi_available def is_torch_cuda_available(): if is_torch_available(): import torch return torch.cuda.is_available() else: return False def is_mamba_ssm_available(): if is_torch_available(): import torch if not torch.cuda.is_available(): return False else: return _is_package_available("mamba_ssm") return False def is_mamba_2_ssm_available(): if is_torch_available(): import torch if not torch.cuda.is_available(): return False else: if _is_package_available("mamba_ssm"): import mamba_ssm if version.parse(mamba_ssm.__version__) >= version.parse("2.0.4"): return True return False def is_causal_conv1d_available(): if is_torch_available(): import torch if not torch.cuda.is_available(): return False return _is_package_available("causal_conv1d") return False def is_mambapy_available(): if is_torch_available(): return _is_package_available("mambapy") return False def is_torch_mps_available(min_version: Optional[str] = None): if is_torch_available(): import torch if hasattr(torch.backends, "mps"): backend_available = torch.backends.mps.is_available() and torch.backends.mps.is_built() if min_version is not None: flag = version.parse(_torch_version) >= version.parse(min_version) backend_available = backend_available and flag return backend_available return False def is_torch_bf16_gpu_available(): if not is_torch_available(): return False import torch return torch.cuda.is_available() and torch.cuda.is_bf16_supported() def is_torch_bf16_cpu_available(): if not is_torch_available(): return False import torch try: # multiple levels of AttributeError depending on the pytorch version so do them all in one check _ = torch.cpu.amp.autocast except AttributeError: return False return True def is_torch_bf16_available(): # the original bf16 check was for gpu only, but later a cpu/bf16 combo has emerged so this util # has become ambiguous and therefore deprecated warnings.warn( "The util is_torch_bf16_available is deprecated, please use is_torch_bf16_gpu_available " "or is_torch_bf16_cpu_available instead according to whether it's used with cpu or gpu", FutureWarning, ) return is_torch_bf16_gpu_available() @lru_cache() def is_torch_fp16_available_on_device(device): if not is_torch_available(): return False import torch try: x = torch.zeros(2, 2, dtype=torch.float16).to(device) _ = x @ x # At this moment, let's be strict of the check: check if `LayerNorm` is also supported on device, because many # models use this layer. batch, sentence_length, embedding_dim = 3, 4, 5 embedding = torch.randn(batch, sentence_length, embedding_dim, dtype=torch.float16, device=device) layer_norm = torch.nn.LayerNorm(embedding_dim, dtype=torch.float16, device=device) _ = layer_norm(embedding) except: # noqa: E722 # TODO: more precise exception matching, if possible. # most backends should return `RuntimeError` however this is not guaranteed. return False return True @lru_cache() def is_torch_bf16_available_on_device(device): if not is_torch_available(): return False import torch if device == "cuda": return is_torch_bf16_gpu_available() try: x = torch.zeros(2, 2, dtype=torch.bfloat16).to(device) _ = x @ x except: # noqa: E722 # TODO: more precise exception matching, if possible. # most backends should return `RuntimeError` however this is not guaranteed. return False return True def is_torch_tf32_available(): if not is_torch_available(): return False import torch if not torch.cuda.is_available() or torch.version.cuda is None: return False if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8: return False if int(torch.version.cuda.split(".")[0]) < 11: return False if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.7"): return False return True def is_torch_fx_available(): return _torch_fx_available def is_peft_available(): return _peft_available def is_bs4_available(): return _bs4_available def is_tf_available(): return _tf_available def is_coloredlogs_available(): return _coloredlogs_available def is_tf2onnx_available(): return _tf2onnx_available def is_onnx_available(): return _onnx_available def is_openai_available(): return _openai_available def is_flax_available(): return _flax_available def is_ftfy_available(): return _ftfy_available def is_g2p_en_available(): return _g2p_en_available @lru_cache() def is_torch_tpu_available(check_device=True): "Checks if `torch_xla` is installed and potentially if a TPU is in the environment" warnings.warn( "`is_torch_tpu_available` is deprecated and will be removed in 4.41.0. " "Please use the `is_torch_xla_available` instead.", FutureWarning, ) if not _torch_available: return False if importlib.util.find_spec("torch_xla") is not None: if check_device: # We need to check if `xla_device` can be found, will raise a RuntimeError if not try: import torch_xla.core.xla_model as xm _ = xm.xla_device() return True except RuntimeError: return False return True return False @lru_cache def is_torch_xla_available(check_is_tpu=False, check_is_gpu=False): """ Check if `torch_xla` is available. To train a native pytorch job in an environment with torch xla installed, set the USE_TORCH_XLA to false. """ assert not (check_is_tpu and check_is_gpu), "The check_is_tpu and check_is_gpu cannot both be true." if not _torch_xla_available: return False import torch_xla if check_is_gpu: return torch_xla.runtime.device_type() in ["GPU", "CUDA"] elif check_is_tpu: return torch_xla.runtime.device_type() == "TPU" return True @lru_cache() def is_torch_neuroncore_available(check_device=True): if importlib.util.find_spec("torch_neuronx") is not None: return is_torch_xla_available() return False @lru_cache() def is_torch_npu_available(check_device=False): "Checks if `torch_npu` is installed and potentially if a NPU is in the environment" if not _torch_available or importlib.util.find_spec("torch_npu") is None: return False import torch import torch_npu # noqa: F401 if check_device: try: # Will raise a RuntimeError if no NPU is found _ = torch.npu.device_count() return torch.npu.is_available() except RuntimeError: return False return hasattr(torch, "npu") and torch.npu.is_available() @lru_cache() def is_torch_mlu_available(check_device=False): """ Checks if `mlu` is available via an `cndev-based` check which won't trigger the drivers and leave mlu uninitialized. """ if not _torch_available or importlib.util.find_spec("torch_mlu") is None: return False import torch import torch_mlu # noqa: F401 pytorch_cndev_based_mlu_check_previous_value = os.environ.get("PYTORCH_CNDEV_BASED_MLU_CHECK") try: os.environ["PYTORCH_CNDEV_BASED_MLU_CHECK"] = str(1) available = torch.mlu.is_available() finally: if pytorch_cndev_based_mlu_check_previous_value: os.environ["PYTORCH_CNDEV_BASED_MLU_CHECK"] = pytorch_cndev_based_mlu_check_previous_value else: os.environ.pop("PYTORCH_CNDEV_BASED_MLU_CHECK", None) return available @lru_cache() def is_torch_musa_available(check_device=False): "Checks if `torch_musa` is installed and potentially if a MUSA is in the environment" if not _torch_available or importlib.util.find_spec("torch_musa") is None: return False import torch import torch_musa # noqa: F401 torch_musa_min_version = "0.33.0" if _accelerate_available and version.parse(_accelerate_version) < version.parse(torch_musa_min_version): return False if check_device: try: # Will raise a RuntimeError if no MUSA is found _ = torch.musa.device_count() return torch.musa.is_available() except RuntimeError: return False return hasattr(torch, "musa") and torch.musa.is_available() def is_torchdynamo_available(): if not is_torch_available(): return False return version.parse(_torch_version) >= version.parse("2.0.0") def is_torch_compile_available(): if not is_torch_available(): return False import torch # We don't do any version check here to support nighlies marked as 1.14. Ultimately needs to check version against # 2.0 but let's do it later. return hasattr(torch, "compile") def is_torchdynamo_compiling(): if not is_torch_available(): return False # Importing torch._dynamo causes issues with PyTorch profiler (https://github.com/pytorch/pytorch/issues/130622) # hence rather relying on `torch.compiler.is_compiling()` when possible (torch>=2.3) try: import torch return torch.compiler.is_compiling() except Exception: try: import torch._dynamo as dynamo # noqa: F401 return dynamo.is_compiling() except Exception: return False def is_torch_tensorrt_fx_available(): if importlib.util.find_spec("torch_tensorrt") is None: return False return importlib.util.find_spec("torch_tensorrt.fx") is not None def is_datasets_available(): return _datasets_available def is_detectron2_available(): return _detectron2_available def is_rjieba_available(): return _rjieba_available def is_psutil_available(): return _psutil_available def is_py3nvml_available(): return _py3nvml_available def is_sacremoses_available(): return _sacremoses_available def is_apex_available(): return _apex_available def is_aqlm_available(): return _aqlm_available def is_av_available(): return _av_available def is_ninja_available(): r""" Code comes from *torch.utils.cpp_extension.is_ninja_available()*. Returns `True` if the [ninja](https://ninja-build.org/) build system is available on the system, `False` otherwise. """ try: subprocess.check_output("ninja --version".split()) except Exception: return False else: return True def is_ipex_available(): def get_major_and_minor_from_version(full_version): return str(version.parse(full_version).major) + "." + str(version.parse(full_version).minor) if not is_torch_available() or not _ipex_available: return False torch_major_and_minor = get_major_and_minor_from_version(_torch_version) ipex_major_and_minor = get_major_and_minor_from_version(_ipex_version) if torch_major_and_minor != ipex_major_and_minor: logger.warning( f"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*," f" but PyTorch {_torch_version} is found. Please switch to the matching version and run again." ) return False return True @lru_cache def is_torch_xpu_available(check_device=False): """ Checks if XPU acceleration is available either via `intel_extension_for_pytorch` or via stock PyTorch (>=2.4) and potentially if a XPU is in the environment """ if not is_torch_available(): return False torch_version = version.parse(_torch_version) if is_ipex_available(): import intel_extension_for_pytorch # noqa: F401 elif torch_version.major < 2 or (torch_version.major == 2 and torch_version.minor < 4): return False import torch if check_device: try: # Will raise a RuntimeError if no XPU is found _ = torch.xpu.device_count() return torch.xpu.is_available() except RuntimeError: return False return hasattr(torch, "xpu") and torch.xpu.is_available() @lru_cache() def is_bitsandbytes_available(): if not is_torch_available() or not _bitsandbytes_available: return False import torch # `bitsandbytes` versions older than 0.43.1 eagerly require CUDA at import time, # so those versions of the library are practically only available when CUDA is too. if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.43.1"): return torch.cuda.is_available() # Newer versions of `bitsandbytes` can be imported on systems without CUDA. return True def is_bitsandbytes_multi_backend_available() -> bool: if not is_bitsandbytes_available(): return False import bitsandbytes as bnb return "multi_backend" in getattr(bnb, "features", set()) def is_flash_attn_2_available(): if not is_torch_available(): return False if not _is_package_available("flash_attn"): return False # Let's add an extra check to see if cuda is available import torch if not (torch.cuda.is_available() or is_torch_mlu_available()): return False if torch.version.cuda: return version.parse(importlib.metadata.version("flash_attn")) >= version.parse("2.1.0") elif torch.version.hip: # TODO: Bump the requirement to 2.1.0 once released in https://github.com/ROCmSoftwarePlatform/flash-attention return version.parse(importlib.metadata.version("flash_attn")) >= version.parse("2.0.4") elif is_torch_mlu_available(): return version.parse(importlib.metadata.version("flash_attn")) >= version.parse("2.3.3") else: return False @lru_cache() def is_flash_attn_greater_or_equal_2_10(): if not _is_package_available("flash_attn"): return False return version.parse(importlib.metadata.version("flash_attn")) >= version.parse("2.1.0") @lru_cache() def is_flash_attn_greater_or_equal(library_version: str): if not _is_package_available("flash_attn"): return False return version.parse(importlib.metadata.version("flash_attn")) >= version.parse(library_version) @lru_cache() def is_torch_greater_or_equal(library_version: str): if not _is_package_available("torch"): return False return version.parse(importlib.metadata.version("torch")) >= version.parse(library_version) def is_torchdistx_available(): return _torchdistx_available def is_faiss_available(): return _faiss_available def is_scipy_available(): return _scipy_available def is_sklearn_available(): return _sklearn_available def is_sentencepiece_available(): return _sentencepiece_available def is_seqio_available(): return _is_seqio_available def is_gguf_available(min_version: str = GGUF_MIN_VERSION): return _is_gguf_available and version.parse(_gguf_version) >= version.parse(min_version) def is_protobuf_available(): if importlib.util.find_spec("google") is None: return False return importlib.util.find_spec("google.protobuf") is not None def is_fsdp_available(min_version: str = FSDP_MIN_VERSION): return is_torch_available() and version.parse(_torch_version) >= version.parse(min_version) def is_optimum_available(): return _optimum_available def is_auto_awq_available(): return _auto_awq_available def is_optimum_quanto_available(): # `importlib.metadata.version` doesn't work with `optimum.quanto`, need to put `optimum_quanto` return _is_optimum_quanto_available def is_compressed_tensors_available(): return _compressed_tensors_available def is_auto_gptq_available(): return _auto_gptq_available def is_eetq_available(): return _eetq_available def is_fbgemm_gpu_available(): return _fbgemm_gpu_available def is_levenshtein_available(): return _levenshtein_available def is_optimum_neuron_available(): return _optimum_available and _is_package_available("optimum.neuron") def is_safetensors_available(): return _safetensors_available def is_tokenizers_available(): return _tokenizers_available @lru_cache def is_vision_available(): _pil_available = importlib.util.find_spec("PIL") is not None if _pil_available: try: package_version = importlib.metadata.version("Pillow") except importlib.metadata.PackageNotFoundError: try: package_version = importlib.metadata.version("Pillow-SIMD") except importlib.metadata.PackageNotFoundError: return False logger.debug(f"Detected PIL version {package_version}") return _pil_available def is_pytesseract_available(): return _pytesseract_available def is_pytest_available(): return _pytest_available def is_spacy_available(): return _spacy_available def is_tensorflow_text_available(): return is_tf_available() and _tensorflow_text_available def is_keras_nlp_available(): return is_tensorflow_text_available() and _keras_nlp_available def is_in_notebook(): try: # Test adapted from tqdm.autonotebook: https://github.com/tqdm/tqdm/blob/master/tqdm/autonotebook.py get_ipython = sys.modules["IPython"].get_ipython if "IPKernelApp" not in get_ipython().config: raise ImportError("console") if "VSCODE_PID" in os.environ: raise ImportError("vscode") if "DATABRICKS_RUNTIME_VERSION" in os.environ and os.environ["DATABRICKS_RUNTIME_VERSION"] < "11.0": # Databricks Runtime 11.0 and above uses IPython kernel by default so it should be compatible with Jupyter notebook # https://docs.microsoft.com/en-us/azure/databricks/notebooks/ipython-kernel raise ImportError("databricks") return importlib.util.find_spec("IPython") is not None except (AttributeError, ImportError, KeyError): return False def is_pytorch_quantization_available(): return _pytorch_quantization_available def is_tensorflow_probability_available(): return _tensorflow_probability_available def is_pandas_available(): return _pandas_available def is_sagemaker_dp_enabled(): # Get the sagemaker specific env variable. sagemaker_params = os.getenv("SM_FRAMEWORK_PARAMS", "{}") try: # Parse it and check the field "sagemaker_distributed_dataparallel_enabled". sagemaker_params = json.loads(sagemaker_params) if not sagemaker_params.get("sagemaker_distributed_dataparallel_enabled", False): return False except json.JSONDecodeError: return False # Lastly, check if the `smdistributed` module is present. return _smdistributed_available def is_sagemaker_mp_enabled(): # Get the sagemaker specific mp parameters from smp_options variable. smp_options = os.getenv("SM_HP_MP_PARAMETERS", "{}") try: # Parse it and check the field "partitions" is included, it is required for model parallel. smp_options = json.loads(smp_options) if "partitions" not in smp_options: return False except json.JSONDecodeError: return False # Get the sagemaker specific framework parameters from mpi_options variable. mpi_options = os.getenv("SM_FRAMEWORK_PARAMS", "{}") try: # Parse it and check the field "sagemaker_distributed_dataparallel_enabled". mpi_options = json.loads(mpi_options) if not mpi_options.get("sagemaker_mpi_enabled", False): return False except json.JSONDecodeError: return False # Lastly, check if the `smdistributed` module is present. return _smdistributed_available def is_training_run_on_sagemaker(): return "SAGEMAKER_JOB_NAME" in os.environ def is_soundfile_availble(): return _soundfile_available def is_timm_available(): return _timm_available def is_natten_available(): return _natten_available def is_nltk_available(): return _nltk_available def is_torchaudio_available(): return _torchaudio_available def is_torchao_available(): return _torchao_available def is_speech_available(): # For now this depends on torchaudio but the exact dependency might evolve in the future. return _torchaudio_available def is_phonemizer_available(): return _phonemizer_available def is_uroman_available(): return _uroman_available def torch_only_method(fn): def wrapper(*args, **kwargs): if not _torch_available: raise ImportError( "You need to install pytorch to use this method or class, " "or activate it with environment variables USE_TORCH=1 and USE_TF=0." ) else: return fn(*args, **kwargs) return wrapper def is_ccl_available(): return _is_ccl_available def is_sudachi_available(): return _sudachipy_available def get_sudachi_version(): return _sudachipy_version def is_sudachi_projection_available(): if not is_sudachi_available(): return False # NOTE: We require sudachipy>=0.6.8 to use projection option in sudachi_kwargs for the constructor of BertJapaneseTokenizer. # - `projection` option is not supported in sudachipy<0.6.8, see https://github.com/WorksApplications/sudachi.rs/issues/230 return version.parse(_sudachipy_version) >= version.parse("0.6.8") def is_jumanpp_available(): return (importlib.util.find_spec("rhoknp") is not None) and (shutil.which("jumanpp") is not None) def is_cython_available(): return importlib.util.find_spec("pyximport") is not None def is_jieba_available(): return _jieba_available def is_jinja_available(): return _jinja_available def is_mlx_available(): return _mlx_available def is_tiktoken_available(): return _tiktoken_available and _blobfile_available def is_liger_kernel_available(): if not _liger_kernel_available: return False return version.parse(importlib.metadata.version("liger_kernel")) >= version.parse("0.3.0") # docstyle-ignore AV_IMPORT_ERROR = """ {0} requires the PyAv library but it was not found in your environment. You can install it with: ``` pip install av ``` Please note that you may need to restart your runtime after installation. """ # docstyle-ignore CV2_IMPORT_ERROR = """ {0} requires the OpenCV library but it was not found in your environment. You can install it with: ``` pip install opencv-python ``` Please note that you may need to restart your runtime after installation. """ # docstyle-ignore DATASETS_IMPORT_ERROR = """ {0} requires the 🤗 Datasets library but it was not found in your environment. You can install it with: ``` pip install datasets ``` In a notebook or a colab, you can install it by executing a cell with ``` !pip install datasets ``` then restarting your kernel. Note that if you have a local folder named `datasets` or a local python file named `datasets.py` in your current working directory, python may try to import this instead of the 🤗 Datasets library. You should rename this folder or that python file if that's the case. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore TOKENIZERS_IMPORT_ERROR = """ {0} requires the 🤗 Tokenizers library but it was not found in your environment. You can install it with: ``` pip install tokenizers ``` In a notebook or a colab, you can install it by executing a cell with ``` !pip install tokenizers ``` Please note that you may need to restart your runtime after installation. """ # docstyle-ignore SENTENCEPIECE_IMPORT_ERROR = """ {0} requires the SentencePiece library but it was not found in your environment. Checkout the instructions on the installation page of its repo: https://github.com/google/sentencepiece#installation and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore PROTOBUF_IMPORT_ERROR = """ {0} requires the protobuf library but it was not found in your environment. Checkout the instructions on the installation page of its repo: https://github.com/protocolbuffers/protobuf/tree/master/python#installation and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore FAISS_IMPORT_ERROR = """ {0} requires the faiss library but it was not found in your environment. Checkout the instructions on the installation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore PYTORCH_IMPORT_ERROR = """ {0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore TORCHVISION_IMPORT_ERROR = """ {0} requires the Torchvision library but it was not found in your environment. Checkout the instructions on the installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore PYTORCH_IMPORT_ERROR_WITH_TF = """ {0} requires the PyTorch library but it was not found in your environment. However, we were able to find a TensorFlow installation. TensorFlow classes begin with "TF", but are otherwise identically named to our PyTorch classes. This means that the TF equivalent of the class you tried to import would be "TF{0}". If you want to use TensorFlow, please use TF classes instead! If you really do want to use PyTorch please go to https://pytorch.org/get-started/locally/ and follow the instructions that match your environment. """ # docstyle-ignore TF_IMPORT_ERROR_WITH_PYTORCH = """ {0} requires the TensorFlow library but it was not found in your environment. However, we were able to find a PyTorch installation. PyTorch classes do not begin with "TF", but are otherwise identically named to our TF classes. If you want to use PyTorch, please use those classes instead! If you really do want to use TensorFlow, please follow the instructions on the installation page https://www.tensorflow.org/install that match your environment. """ # docstyle-ignore BS4_IMPORT_ERROR = """ {0} requires the Beautiful Soup library but it was not found in your environment. You can install it with pip: `pip install beautifulsoup4`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore SKLEARN_IMPORT_ERROR = """ {0} requires the scikit-learn library but it was not found in your environment. You can install it with: ``` pip install -U scikit-learn ``` In a notebook or a colab, you can install it by executing a cell with ``` !pip install -U scikit-learn ``` Please note that you may need to restart your runtime after installation. """ # docstyle-ignore TENSORFLOW_IMPORT_ERROR = """ {0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the installation page: https://www.tensorflow.org/install and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore DETECTRON2_IMPORT_ERROR = """ {0} requires the detectron2 library but it was not found in your environment. Checkout the instructions on the installation page: https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore FLAX_IMPORT_ERROR = """ {0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the installation page: https://github.com/google/flax and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore FTFY_IMPORT_ERROR = """ {0} requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. """ LEVENSHTEIN_IMPORT_ERROR = """ {0} requires the python-Levenshtein library but it was not found in your environment. You can install it with pip: `pip install python-Levenshtein`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore G2P_EN_IMPORT_ERROR = """ {0} requires the g2p-en library but it was not found in your environment. You can install it with pip: `pip install g2p-en`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore PYTORCH_QUANTIZATION_IMPORT_ERROR = """ {0} requires the pytorch-quantization library but it was not found in your environment. You can install it with pip: `pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com` Please note that you may need to restart your runtime after installation. """ # docstyle-ignore TENSORFLOW_PROBABILITY_IMPORT_ERROR = """ {0} requires the tensorflow_probability library but it was not found in your environment. You can install it with pip as explained here: https://github.com/tensorflow/probability. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore TENSORFLOW_TEXT_IMPORT_ERROR = """ {0} requires the tensorflow_text library but it was not found in your environment. You can install it with pip as explained here: https://www.tensorflow.org/text/guide/tf_text_intro. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore TORCHAUDIO_IMPORT_ERROR = """ {0} requires the torchaudio library but it was not found in your environment. Please install it and restart your runtime. """ # docstyle-ignore PANDAS_IMPORT_ERROR = """ {0} requires the pandas library but it was not found in your environment. You can install it with pip as explained here: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore PHONEMIZER_IMPORT_ERROR = """ {0} requires the phonemizer library but it was not found in your environment. You can install it with pip: `pip install phonemizer`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore UROMAN_IMPORT_ERROR = """ {0} requires the uroman library but it was not found in your environment. You can install it with pip: `pip install uroman`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore SACREMOSES_IMPORT_ERROR = """ {0} requires the sacremoses library but it was not found in your environment. You can install it with pip: `pip install sacremoses`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore SCIPY_IMPORT_ERROR = """ {0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install scipy`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore SPEECH_IMPORT_ERROR = """ {0} requires the torchaudio library but it was not found in your environment. You can install it with pip: `pip install torchaudio`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore TIMM_IMPORT_ERROR = """ {0} requires the timm library but it was not found in your environment. You can install it with pip: `pip install timm`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore NATTEN_IMPORT_ERROR = """ {0} requires the natten library but it was not found in your environment. You can install it by referring to: shi-labs.com/natten . You can also install it with pip (may take longer to build): `pip install natten`. Please note that you may need to restart your runtime after installation. """ NUMEXPR_IMPORT_ERROR = """ {0} requires the numexpr library but it was not found in your environment. You can install it by referring to: https://numexpr.readthedocs.io/en/latest/index.html. """ # docstyle-ignore NLTK_IMPORT_ERROR = """ {0} requires the NLTK library but it was not found in your environment. You can install it by referring to: https://www.nltk.org/install.html. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore VISION_IMPORT_ERROR = """ {0} requires the PIL library but it was not found in your environment. You can install it with pip: `pip install pillow`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore PYTESSERACT_IMPORT_ERROR = """ {0} requires the PyTesseract library but it was not found in your environment. You can install it with pip: `pip install pytesseract`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore PYCTCDECODE_IMPORT_ERROR = """ {0} requires the pyctcdecode library but it was not found in your environment. You can install it with pip: `pip install pyctcdecode`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore ACCELERATE_IMPORT_ERROR = """ {0} requires the accelerate library >= {ACCELERATE_MIN_VERSION} it was not found in your environment. You can install or update it with pip: `pip install --upgrade accelerate`. Please note that you may need to restart your runtime after installation. """ # docstyle-ignore CCL_IMPORT_ERROR = """ {0} requires the torch ccl library but it was not found in your environment. You can install it with pip: `pip install oneccl_bind_pt -f https://developer.intel.com/ipex-whl-stable` Please note that you may need to restart your runtime after installation. """ # docstyle-ignore ESSENTIA_IMPORT_ERROR = """ {0} requires essentia library. But that was not found in your environment. You can install them with pip: `pip install essentia==2.1b6.dev1034` Please note that you may need to restart your runtime after installation. """ # docstyle-ignore LIBROSA_IMPORT_ERROR = """ {0} requires thes librosa library. But that was not found in your environment. You can install them with pip: `pip install librosa` Please note that you may need to restart your runtime after installation. """ # docstyle-ignore PRETTY_MIDI_IMPORT_ERROR = """ {0} requires thes pretty_midi library. But that was not found in your environment. You can install them with pip: `pip install pretty_midi` Please note that you may need to restart your runtime after installation. """ CYTHON_IMPORT_ERROR = """ {0} requires the Cython library but it was not found in your environment. You can install it with pip: `pip install Cython`. Please note that you may need to restart your runtime after installation. """ JIEBA_IMPORT_ERROR = """ {0} requires the jieba library but it was not found in your environment. You can install it with pip: `pip install jieba`. Please note that you may need to restart your runtime after installation. """ PEFT_IMPORT_ERROR = """ {0} requires the peft library but it was not found in your environment. You can install it with pip: `pip install peft`. Please note that you may need to restart your runtime after installation. """ JINJA_IMPORT_ERROR = """ {0} requires the jinja library but it was not found in your environment. You can install it with pip: `pip install jinja2`. Please note that you may need to restart your runtime after installation. """ BACKENDS_MAPPING = OrderedDict( [ ("av", (is_av_available, AV_IMPORT_ERROR)), ("bs4", (is_bs4_available, BS4_IMPORT_ERROR)), ("cv2", (is_cv2_available, CV2_IMPORT_ERROR)), ("datasets", (is_datasets_available, DATASETS_IMPORT_ERROR)), ("detectron2", (is_detectron2_available, DETECTRON2_IMPORT_ERROR)), ("essentia", (is_essentia_available, ESSENTIA_IMPORT_ERROR)), ("faiss", (is_faiss_available, FAISS_IMPORT_ERROR)), ("flax", (is_flax_available, FLAX_IMPORT_ERROR)), ("ftfy", (is_ftfy_available, FTFY_IMPORT_ERROR)), ("g2p_en", (is_g2p_en_available, G2P_EN_IMPORT_ERROR)), ("pandas", (is_pandas_available, PANDAS_IMPORT_ERROR)), ("phonemizer", (is_phonemizer_available, PHONEMIZER_IMPORT_ERROR)), ("uroman", (is_uroman_available, UROMAN_IMPORT_ERROR)), ("pretty_midi", (is_pretty_midi_available, PRETTY_MIDI_IMPORT_ERROR)), ("levenshtein", (is_levenshtein_available, LEVENSHTEIN_IMPORT_ERROR)), ("librosa", (is_librosa_available, LIBROSA_IMPORT_ERROR)), ("protobuf", (is_protobuf_available, PROTOBUF_IMPORT_ERROR)), ("pyctcdecode", (is_pyctcdecode_available, PYCTCDECODE_IMPORT_ERROR)), ("pytesseract", (is_pytesseract_available, PYTESSERACT_IMPORT_ERROR)), ("sacremoses", (is_sacremoses_available, SACREMOSES_IMPORT_ERROR)), ("pytorch_quantization", (is_pytorch_quantization_available, PYTORCH_QUANTIZATION_IMPORT_ERROR)), ("sentencepiece", (is_sentencepiece_available, SENTENCEPIECE_IMPORT_ERROR)), ("sklearn", (is_sklearn_available, SKLEARN_IMPORT_ERROR)), ("speech", (is_speech_available, SPEECH_IMPORT_ERROR)), ("tensorflow_probability", (is_tensorflow_probability_available, TENSORFLOW_PROBABILITY_IMPORT_ERROR)), ("tf", (is_tf_available, TENSORFLOW_IMPORT_ERROR)), ("tensorflow_text", (is_tensorflow_text_available, TENSORFLOW_TEXT_IMPORT_ERROR)), ("timm", (is_timm_available, TIMM_IMPORT_ERROR)), ("torchaudio", (is_torchaudio_available, TORCHAUDIO_IMPORT_ERROR)), ("natten", (is_natten_available, NATTEN_IMPORT_ERROR)), ("nltk", (is_nltk_available, NLTK_IMPORT_ERROR)), ("tokenizers", (is_tokenizers_available, TOKENIZERS_IMPORT_ERROR)), ("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)), ("torchvision", (is_torchvision_available, TORCHVISION_IMPORT_ERROR)), ("vision", (is_vision_available, VISION_IMPORT_ERROR)), ("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)), ("accelerate", (is_accelerate_available, ACCELERATE_IMPORT_ERROR)), ("oneccl_bind_pt", (is_ccl_available, CCL_IMPORT_ERROR)), ("cython", (is_cython_available, CYTHON_IMPORT_ERROR)), ("jieba", (is_jieba_available, JIEBA_IMPORT_ERROR)), ("peft", (is_peft_available, PEFT_IMPORT_ERROR)), ("jinja", (is_jinja_available, JINJA_IMPORT_ERROR)), ] ) def requires_backends(obj, backends): if not isinstance(backends, (list, tuple)): backends = [backends] name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__ # Raise an error for users who might not realize that classes without "TF" are torch-only if "torch" in backends and "tf" not in backends and not is_torch_available() and is_tf_available(): raise ImportError(PYTORCH_IMPORT_ERROR_WITH_TF.format(name)) # Raise the inverse error for PyTorch users trying to load TF classes if "tf" in backends and "torch" not in backends and is_torch_available() and not is_tf_available(): raise ImportError(TF_IMPORT_ERROR_WITH_PYTORCH.format(name)) checks = (BACKENDS_MAPPING[backend] for backend in backends) failed = [msg.format(name) for available, msg in checks if not available()] if failed: raise ImportError("".join(failed)) class DummyObject(type): """ Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by `requires_backend` each time a user tries to access any method of that class. """ def __getattribute__(cls, key): if key.startswith("_") and key != "_from_config": return super().__getattribute__(key) requires_backends(cls, cls._backends) def is_torch_fx_proxy(x): if is_torch_fx_available(): import torch.fx return isinstance(x, torch.fx.Proxy) return False BACKENDS_T = FrozenSet[str] IMPORT_STRUCTURE_T = Dict[BACKENDS_T, Dict[str, Set[str]]] class _LazyModule(ModuleType): """ Module class that surfaces all objects but only performs associated imports when the objects are requested. """ # Very heavily inspired by optuna.integration._IntegrationModule # https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py def __init__( self, name: str, module_file: str, import_structure: IMPORT_STRUCTURE_T, module_spec: importlib.machinery.ModuleSpec = None, extra_objects: Dict[str, object] = None, ): super().__init__(name) self._object_missing_backend = {} if any(isinstance(key, frozenset) for key in import_structure.keys()): self._modules = set() self._class_to_module = {} self.__all__ = [] _import_structure = {} for backends, module in import_structure.items(): missing_backends = [] for backend in backends: if backend not in BACKENDS_MAPPING: raise ValueError( f"Error: the following backend: '{backend}' was specified around object {module} but isn't specified in the backends mapping." ) callable, error = BACKENDS_MAPPING[backend] if not callable(): missing_backends.append(backend) self._modules = self._modules.union(set(module.keys())) for key, values in module.items(): if len(missing_backends): self._object_missing_backend[key] = missing_backends for value in values: self._class_to_module[value] = key if len(missing_backends): self._object_missing_backend[value] = missing_backends _import_structure.setdefault(key, []).extend(values) # Needed for autocompletion in an IDE self.__all__.extend(list(module.keys()) + list(chain(*module.values()))) self.__file__ = module_file self.__spec__ = module_spec self.__path__ = [os.path.dirname(module_file)] self._objects = {} if extra_objects is None else extra_objects self._name = name self._import_structure = _import_structure # This can be removed once every exportable object has a `export()` export. else: self._modules = set(import_structure.keys()) self._class_to_module = {} for key, values in import_structure.items(): for value in values: self._class_to_module[value] = key # Needed for autocompletion in an IDE self.__all__ = list(import_structure.keys()) + list(chain(*import_structure.values())) self.__file__ = module_file self.__spec__ = module_spec self.__path__ = [os.path.dirname(module_file)] self._objects = {} if extra_objects is None else extra_objects self._name = name self._import_structure = import_structure # Needed for autocompletion in an IDE def __dir__(self): result = super().__dir__() # The elements of self.__all__ that are submodules may or may not be in the dir already, depending on whether # they have been accessed or not. So we only add the elements of self.__all__ that are not already in the dir. for attr in self.__all__: if attr not in result: result.append(attr) return result def __getattr__(self, name: str) -> Any: if name in self._objects: return self._objects[name] if name in self._object_missing_backend.keys(): missing_backends = self._object_missing_backend[name] class Placeholder(metaclass=DummyObject): _backends = missing_backends def __init__(self, *args, **kwargs): requires_backends(self, missing_backends) Placeholder.__name__ = name Placeholder.__module__ = self.__spec__ value = Placeholder elif name in self._class_to_module.keys(): module = self._get_module(self._class_to_module[name]) value = getattr(module, name) elif name in self._modules: value = self._get_module(name) else: raise AttributeError(f"module {self.__name__} has no attribute {name}") setattr(self, name, value) return value def _get_module(self, module_name: str): try: return importlib.import_module("." + module_name, self.__name__) except Exception as e: raise RuntimeError( f"Failed to import {self.__name__}.{module_name} because of the following error (look up to see its" f" traceback):\n{e}" ) from e def __reduce__(self): return (self.__class__, (self._name, self.__file__, self._import_structure)) class OptionalDependencyNotAvailable(BaseException): """Internally used error class for signalling an optional dependency was not found.""" def direct_transformers_import(path: str, file="__init__.py") -> ModuleType: """Imports transformers directly Args: path (`str`): The path to the source file file (`str`, *optional*): The file to join with the path. Defaults to "__init__.py". Returns: `ModuleType`: The resulting imported module """ name = "transformers" location = os.path.join(path, file) spec = importlib.util.spec_from_file_location(name, location, submodule_search_locations=[path]) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) module = sys.modules[name] return module def export(*, backends=()): """ This decorator enables two things: - Attaching a `__backends` tuple to an object to see what are the necessary backends for it to execute correctly without instantiating it - The '@export' string is used to dynamically import objects """ for backend in backends: if backend not in BACKENDS_MAPPING: raise ValueError(f"Backend should be defined in the BACKENDS_MAPPING. Offending backend: {backend}") if not isinstance(backends, tuple): raise ValueError("Backends should be a tuple.") def inner_fn(fun): fun.__backends = backends return fun return inner_fn BASE_FILE_REQUIREMENTS = { lambda e: "modeling_tf_" in e: ("tf",), lambda e: "modeling_flax_" in e: ("flax",), lambda e: "modeling_" in e: ("torch",), lambda e: e.startswith("tokenization_") and e.endswith("_fast"): ("tokenizers",), } def fetch__all__(file_content): """ Returns the content of the __all__ variable in the file content. Returns None if not defined, otherwise returns a list of strings. """ if "__all__" not in file_content: return [] lines = file_content.splitlines() for index, line in enumerate(lines): if line.startswith("__all__"): start_index = index lines = lines[start_index:] if not lines[0].startswith("__all__"): raise ValueError( "fetch__all__ accepts a list of lines, with the first line being the __all__ variable declaration" ) # __all__ is defined on a single line if lines[0].endswith("]"): return [obj.strip("\"' ") for obj in lines[0].split("=")[1].strip(" []").split(",")] # __all__ is defined on multiple lines else: _all = [] for __all__line_index in range(1, len(lines)): if lines[__all__line_index].strip() == "]": return _all else: _all.append(lines[__all__line_index].strip("\"', ")) return _all @lru_cache() def create_import_structure_from_path(module_path): """ This method takes the path to a file/a folder and returns the import structure. If a file is given, it will return the import structure of the parent folder. Import structures are designed to be digestible by `_LazyModule` objects. They are created from the __all__ definitions in each files as well as the `@export` decorators above methods and objects. The import structure allows explicit display of the required backends for a given object. These backends are specified in two ways: 1. Through their `@export`, if they are exported with that decorator. This `@export` decorator accepts a `backend` tuple kwarg mentioning which backends are required to run this object. 2. If an object is defined in a file with "default" backends, it will have, at a minimum, this backend specified. The default backends are defined according to the filename: - If a file is named like `modeling_*.py`, it will have a `torch` backend - If a file is named like `modeling_tf_*.py`, it will have a `tf` backend - If a file is named like `modeling_flax_*.py`, it will have a `flax` backend - If a file is named like `tokenization_*_fast.py`, it will have a `tokenizers` backend Backends serve the purpose of displaying a clear error message to the user in case the backends are not installed. Should an object be imported without its required backends being in the environment, any attempt to use the object will raise an error mentioning which backend(s) should be added to the environment in order to use that object. Here's an example of an input import structure at the src.transformers.models level: { 'albert': { frozenset(): { 'configuration_albert': {'AlbertConfig', 'AlbertOnnxConfig'} }, frozenset({'tokenizers'}): { 'tokenization_albert_fast': {'AlbertTokenizerFast'} }, }, 'align': { frozenset(): { 'configuration_align': {'AlignConfig', 'AlignTextConfig', 'AlignVisionConfig'}, 'processing_align': {'AlignProcessor'} }, }, 'altclip': { frozenset(): { 'configuration_altclip': {'AltCLIPConfig', 'AltCLIPTextConfig', 'AltCLIPVisionConfig'}, 'processing_altclip': {'AltCLIPProcessor'}, } } } """ import_structure = {} if os.path.isdir(module_path): directory = module_path adjacent_modules = [] for f in os.listdir(module_path): if f != "__pycache__" and os.path.isdir(os.path.join(module_path, f)): import_structure[f] = create_import_structure_from_path(os.path.join(module_path, f)) elif not os.path.isdir(os.path.join(directory, f)): adjacent_modules.append(f) else: directory = os.path.dirname(module_path) adjacent_modules = [f for f in os.listdir(directory) if not os.path.isdir(os.path.join(directory, f))] # We're only taking a look at files different from __init__.py # We could theoretically export things directly from the __init__.py # files, but this is not supported at this time. if "__init__.py" in adjacent_modules: adjacent_modules.remove("__init__.py") # Modular files should not be imported def find_substring(substring, list_): return any(substring in x for x in list_) if find_substring("modular_", adjacent_modules) and find_substring("modeling_", adjacent_modules): adjacent_modules = [module for module in adjacent_modules if "modular_" not in module] module_requirements = {} for module_name in adjacent_modules: # Only modules ending in `.py` are accepted here. if not module_name.endswith(".py"): continue with open(os.path.join(directory, module_name), encoding="utf-8") as f: file_content = f.read() # Remove the .py suffix module_name = module_name[:-3] previous_line = "" previous_index = 0 # Some files have some requirements by default. # For example, any file named `modeling_tf_xxx.py` # should have TensorFlow as a required backend. base_requirements = () for string_check, requirements in BASE_FILE_REQUIREMENTS.items(): if string_check(module_name): base_requirements = requirements break # Objects that have a `@export` assigned to them will get exported # with the backends specified in the decorator as well as the file backends. exported_objects = set() if "@export" in file_content: lines = file_content.split("\n") for index, line in enumerate(lines): # This allows exporting items with other decorators. We'll take a look # at the line that follows at the same indentation level. if line.startswith((" ", "\t", "@", ")")) and not line.startswith("@export"): continue # Skipping line enables putting whatever we want between the # export() call and the actual class/method definition. # This is what enables having # Copied from statements, docs, etc. skip_line = False if "@export" in previous_line: skip_line = False # Backends are defined on the same line as export if "backends" in previous_line: backends_string = previous_line.split("backends=")[1].split("(")[1].split(")")[0] backends = tuple(sorted([b.strip("'\",") for b in backends_string.split(", ") if b])) # Backends are defined in the lines following export, for example such as: # @export( # backends=( # "sentencepiece", # "torch", # "tf", # ) # ) # # or # # @export( # backends=( # "sentencepiece", "tf" # ) # ) elif "backends" in lines[previous_index + 1]: backends = [] for backend_line in lines[previous_index:index]: if "backends" in backend_line: backend_line = backend_line.split("=")[1] if '"' in backend_line or "'" in backend_line: if ", " in backend_line: backends.extend(backend.strip("()\"', ") for backend in backend_line.split(", ")) else: backends.append(backend_line.strip("()\"', ")) # If the line is only a ')', then we reached the end of the backends and we break. if backend_line.strip() == ")": break backends = tuple(backends) # No backends are registered for export else: backends = () backends = frozenset(backends + base_requirements) if backends not in module_requirements: module_requirements[backends] = {} if module_name not in module_requirements[backends]: module_requirements[backends][module_name] = set() if not line.startswith("class") and not line.startswith("def"): skip_line = True else: start_index = 6 if line.startswith("class") else 4 object_name = line[start_index:].split("(")[0].strip(":") module_requirements[backends][module_name].add(object_name) exported_objects.add(object_name) if not skip_line: previous_line = line previous_index = index # All objects that are in __all__ should be exported by default. # These objects are exported with the file backends. if "__all__" in file_content: for _all_object in fetch__all__(file_content): if _all_object not in exported_objects: backends = frozenset(base_requirements) if backends not in module_requirements: module_requirements[backends] = {} if module_name not in module_requirements[backends]: module_requirements[backends][module_name] = set() module_requirements[backends][module_name].add(_all_object) import_structure = {**module_requirements, **import_structure} return import_structure def spread_import_structure(nested_import_structure): """ This method takes as input an unordered import structure and brings the required backends at the top-level, aggregating modules and objects under their required backends. Here's an example of an input import structure at the src.transformers.models level: { 'albert': { frozenset(): { 'configuration_albert': {'AlbertConfig', 'AlbertOnnxConfig'} }, frozenset({'tokenizers'}): { 'tokenization_albert_fast': {'AlbertTokenizerFast'} }, }, 'align': { frozenset(): { 'configuration_align': {'AlignConfig', 'AlignTextConfig', 'AlignVisionConfig'}, 'processing_align': {'AlignProcessor'} }, }, 'altclip': { frozenset(): { 'configuration_altclip': {'AltCLIPConfig', 'AltCLIPTextConfig', 'AltCLIPVisionConfig'}, 'processing_altclip': {'AltCLIPProcessor'}, } } } Here's an example of an output import structure at the src.transformers.models level: { frozenset({'tokenizers'}): { 'albert.tokenization_albert_fast': {'AlbertTokenizerFast'} }, frozenset(): { 'albert.configuration_albert': {'AlbertConfig', 'AlbertOnnxConfig'}, 'align.processing_align': {'AlignProcessor'}, 'align.configuration_align': {'AlignConfig', 'AlignTextConfig', 'AlignVisionConfig'}, 'altclip.configuration_altclip': {'AltCLIPConfig', 'AltCLIPTextConfig', 'AltCLIPVisionConfig'}, 'altclip.processing_altclip': {'AltCLIPProcessor'} } } """ def propagate_frozenset(unordered_import_structure): tuple_first_import_structure = {} for _key, _value in unordered_import_structure.items(): if not isinstance(_value, dict): tuple_first_import_structure[_key] = _value elif any(isinstance(v, frozenset) for v in _value.keys()): # Here we want to switch around key and v for k, v in _value.items(): if isinstance(k, frozenset): if k not in tuple_first_import_structure: tuple_first_import_structure[k] = {} tuple_first_import_structure[k][_key] = v else: tuple_first_import_structure[_key] = propagate_frozenset(_value) return tuple_first_import_structure def flatten_dict(_dict, previous_key=None): items = [] for _key, _value in _dict.items(): _key = f"{previous_key}.{_key}" if previous_key is not None else _key if isinstance(_value, dict): items.extend(flatten_dict(_value, _key).items()) else: items.append((_key, _value)) return dict(items) # The tuples contain the necessary backends. We want these first, so we propagate them up the # import structure. ordered_import_structure = nested_import_structure # 6 is a number that gives us sufficient depth to go through all files and foreseeable folder depths # while not taking too long to parse. for i in range(6): ordered_import_structure = propagate_frozenset(ordered_import_structure) # We then flatten the dict so that it references a module path. flattened_import_structure = {} for key, value in ordered_import_structure.copy().items(): if isinstance(key, str): del ordered_import_structure[key] else: flattened_import_structure[key] = flatten_dict(value) return flattened_import_structure def define_import_structure(module_path: str) -> IMPORT_STRUCTURE_T: """ This method takes a module_path as input and creates an import structure digestible by a _LazyModule. Here's an example of an output import structure at the src.transformers.models level: { frozenset({'tokenizers'}): { 'albert.tokenization_albert_fast': {'AlbertTokenizerFast'} }, frozenset(): { 'albert.configuration_albert': {'AlbertConfig', 'AlbertOnnxConfig'}, 'align.processing_align': {'AlignProcessor'}, 'align.configuration_align': {'AlignConfig', 'AlignTextConfig', 'AlignVisionConfig'}, 'altclip.configuration_altclip': {'AltCLIPConfig', 'AltCLIPTextConfig', 'AltCLIPVisionConfig'}, 'altclip.processing_altclip': {'AltCLIPProcessor'} } } The import structure is a dict defined with frozensets as keys, and dicts of strings to sets of objects. """ import_structure = create_import_structure_from_path(module_path) return spread_import_structure(import_structure)