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|
| | import os |
| | from dataclasses import dataclass, field |
| | from enum import Enum |
| | from typing import Any, Dict, Optional, Tuple, Union |
| |
|
| | from haystack import logging |
| | from haystack.lazy_imports import LazyImport |
| |
|
| | logger = logging.getLogger(__name__) |
| |
|
| | with LazyImport( |
| | message="PyTorch must be installed to use torch.device or use GPU support in HuggingFace transformers. " |
| | "Run 'pip install \"transformers[torch]\"'" |
| | ) as torch_import: |
| | import torch |
| |
|
| |
|
| | class DeviceType(Enum): |
| | """ |
| | Represents device types supported by Haystack. |
| | |
| | This also includes devices that are not directly used by models - for example, the disk device is exclusively used |
| | in device maps for frameworks that support offloading model weights to disk. |
| | """ |
| |
|
| | CPU = "cpu" |
| | GPU = "cuda" |
| | DISK = "disk" |
| | MPS = "mps" |
| |
|
| | def __str__(self): |
| | return self.value |
| |
|
| | @staticmethod |
| | def from_str(string: str) -> "DeviceType": |
| | """ |
| | Create a device type from a string. |
| | |
| | :param string: |
| | The string to convert. |
| | :returns: |
| | The device type. |
| | """ |
| | mapping = {e.value: e for e in DeviceType} |
| | _type = mapping.get(string) |
| | if _type is None: |
| | raise ValueError(f"Unknown device type string '{string}'") |
| | return _type |
| |
|
| |
|
| | @dataclass |
| | class Device: |
| | """ |
| | A generic representation of a device. |
| | |
| | :param type: |
| | The device type. |
| | :param id: |
| | The optional device id. |
| | """ |
| |
|
| | type: DeviceType |
| | id: Optional[int] = field(default=None) |
| |
|
| | def __init__(self, type: DeviceType, id: Optional[int] = None): |
| | """ |
| | Create a generic device. |
| | |
| | :param type: |
| | The device type. |
| | :param id: |
| | The device id. |
| | """ |
| | if id is not None and id < 0: |
| | raise ValueError(f"Device id must be >= 0, got {id}") |
| |
|
| | self.type = type |
| | self.id = id |
| |
|
| | def __str__(self): |
| | if self.id is None: |
| | return str(self.type) |
| | else: |
| | return f"{self.type}:{self.id}" |
| |
|
| | @staticmethod |
| | def cpu() -> "Device": |
| | """ |
| | Create a generic CPU device. |
| | |
| | :returns: |
| | The CPU device. |
| | """ |
| | return Device(DeviceType.CPU) |
| |
|
| | @staticmethod |
| | def gpu(id: int = 0) -> "Device": |
| | """ |
| | Create a generic GPU device. |
| | |
| | :param id: |
| | The GPU id. |
| | :returns: |
| | The GPU device. |
| | """ |
| | return Device(DeviceType.GPU, id) |
| |
|
| | @staticmethod |
| | def disk() -> "Device": |
| | """ |
| | Create a generic disk device. |
| | |
| | :returns: |
| | The disk device. |
| | """ |
| | return Device(DeviceType.DISK) |
| |
|
| | @staticmethod |
| | def mps() -> "Device": |
| | """ |
| | Create a generic Apple Metal Performance Shader device. |
| | |
| | :returns: |
| | The MPS device. |
| | """ |
| | return Device(DeviceType.MPS) |
| |
|
| | @staticmethod |
| | def from_str(string: str) -> "Device": |
| | """ |
| | Create a generic device from a string. |
| | |
| | :returns: |
| | The device. |
| | |
| | """ |
| | device_type_str, device_id = _split_device_string(string) |
| | return Device(DeviceType.from_str(device_type_str), device_id) |
| |
|
| |
|
| | @dataclass |
| | class DeviceMap: |
| | """ |
| | A generic mapping from strings to devices. |
| | |
| | The semantics of the strings are dependent on target framework. Primarily used to deploy HuggingFace models to |
| | multiple devices. |
| | |
| | :param mapping: |
| | Dictionary mapping strings to devices. |
| | """ |
| |
|
| | mapping: Dict[str, Device] = field(default_factory=dict, hash=False) |
| |
|
| | def __getitem__(self, key: str) -> Device: |
| | return self.mapping[key] |
| |
|
| | def __setitem__(self, key: str, value: Device): |
| | self.mapping[key] = value |
| |
|
| | def __contains__(self, key: str) -> bool: |
| | return key in self.mapping |
| |
|
| | def __len__(self) -> int: |
| | return len(self.mapping) |
| |
|
| | def __iter__(self): |
| | return iter(self.mapping.items()) |
| |
|
| | def to_dict(self) -> Dict[str, str]: |
| | """ |
| | Serialize the mapping to a JSON-serializable dictionary. |
| | |
| | :returns: |
| | The serialized mapping. |
| | """ |
| | return {key: str(device) for key, device in self.mapping.items()} |
| |
|
| | @property |
| | def first_device(self) -> Optional[Device]: |
| | """ |
| | Return the first device in the mapping, if any. |
| | |
| | :returns: |
| | The first device. |
| | """ |
| | if not self.mapping: |
| | return None |
| | else: |
| | return next(iter(self.mapping.values())) |
| |
|
| | @staticmethod |
| | def from_dict(dict: Dict[str, str]) -> "DeviceMap": |
| | """ |
| | Create a generic device map from a JSON-serialized dictionary. |
| | |
| | :param dict: |
| | The serialized mapping. |
| | :returns: |
| | The generic device map. |
| | """ |
| | mapping = {} |
| | for key, device_str in dict.items(): |
| | mapping[key] = Device.from_str(device_str) |
| | return DeviceMap(mapping) |
| |
|
| | @staticmethod |
| | def from_hf(hf_device_map: Dict[str, Union[int, str, "torch.device"]]) -> "DeviceMap": |
| | """ |
| | Create a generic device map from a HuggingFace device map. |
| | |
| | :param hf_device_map: |
| | The HuggingFace device map. |
| | :returns: |
| | The deserialized device map. |
| | """ |
| | mapping = {} |
| | for key, device in hf_device_map.items(): |
| | if isinstance(device, int): |
| | mapping[key] = Device(DeviceType.GPU, device) |
| | elif isinstance(device, str): |
| | device_type, device_id = _split_device_string(device) |
| | mapping[key] = Device(DeviceType.from_str(device_type), device_id) |
| | elif isinstance(device, torch.device): |
| | device_type = device.type |
| | device_id = device.index |
| | mapping[key] = Device(DeviceType.from_str(device_type), device_id) |
| | else: |
| | raise ValueError( |
| | f"Couldn't convert HuggingFace device map - unexpected device '{str(device)}' for '{key}'" |
| | ) |
| | return DeviceMap(mapping) |
| |
|
| |
|
| | @dataclass(frozen=True) |
| | class ComponentDevice: |
| | """ |
| | A representation of a device for a component. |
| | |
| | This can be either a single device or a device map. |
| | """ |
| |
|
| | _single_device: Optional[Device] = field(default=None) |
| | _multiple_devices: Optional[DeviceMap] = field(default=None) |
| |
|
| | @classmethod |
| | def from_str(cls, device_str: str) -> "ComponentDevice": |
| | """ |
| | Create a component device representation from a device string. |
| | |
| | The device string can only represent a single device. |
| | |
| | :param device_str: |
| | The device string. |
| | :returns: |
| | The component device representation. |
| | """ |
| | device = Device.from_str(device_str) |
| | return cls.from_single(device) |
| |
|
| | @classmethod |
| | def from_single(cls, device: Device) -> "ComponentDevice": |
| | """ |
| | Create a component device representation from a single device. |
| | |
| | Disks cannot be used as single devices. |
| | |
| | :param device: |
| | The device. |
| | :returns: |
| | The component device representation. |
| | """ |
| | if device.type == DeviceType.DISK: |
| | raise ValueError("The disk device can only be used as a part of device maps") |
| |
|
| | return cls(_single_device=device) |
| |
|
| | @classmethod |
| | def from_multiple(cls, device_map: DeviceMap) -> "ComponentDevice": |
| | """ |
| | Create a component device representation from a device map. |
| | |
| | :param device_map: |
| | The device map. |
| | :returns: |
| | The component device representation. |
| | """ |
| | return cls(_multiple_devices=device_map) |
| |
|
| | def _validate(self): |
| | """ |
| | Validate the component device representation. |
| | """ |
| | if not (self._single_device is not None) ^ (self._multiple_devices is not None): |
| | raise ValueError( |
| | "The component device can neither be empty nor contain both a single device and a device map" |
| | ) |
| |
|
| | def to_torch(self) -> "torch.device": |
| | """ |
| | Convert the component device representation to PyTorch format. |
| | |
| | Device maps are not supported. |
| | |
| | :returns: |
| | The PyTorch device representation. |
| | """ |
| | self._validate() |
| |
|
| | if self._single_device is None: |
| | raise ValueError("Only single devices can be converted to PyTorch format") |
| |
|
| | torch_import.check() |
| | assert self._single_device is not None |
| | return torch.device(str(self._single_device)) |
| |
|
| | def to_torch_str(self) -> str: |
| | """ |
| | Convert the component device representation to PyTorch string format. |
| | |
| | Device maps are not supported. |
| | |
| | :returns: |
| | The PyTorch device string representation. |
| | """ |
| | self._validate() |
| |
|
| | if self._single_device is None: |
| | raise ValueError("Only single devices can be converted to PyTorch format") |
| |
|
| | assert self._single_device is not None |
| | return str(self._single_device) |
| |
|
| | def to_spacy(self) -> int: |
| | """ |
| | Convert the component device representation to spaCy format. |
| | |
| | Device maps are not supported. |
| | |
| | :returns: |
| | The spaCy device representation. |
| | """ |
| | self._validate() |
| |
|
| | if self._single_device is None: |
| | raise ValueError("Only single devices can be converted to spaCy format") |
| |
|
| | assert self._single_device is not None |
| | if self._single_device.type == DeviceType.GPU: |
| | assert self._single_device.id is not None |
| | return self._single_device.id |
| | else: |
| | return -1 |
| |
|
| | def to_hf(self) -> Union[Union[int, str], Dict[str, Union[int, str]]]: |
| | """ |
| | Convert the component device representation to HuggingFace format. |
| | |
| | :returns: |
| | The HuggingFace device representation. |
| | """ |
| | self._validate() |
| |
|
| | def convert_device(device: Device, *, gpu_id_only: bool = False) -> Union[int, str]: |
| | if gpu_id_only and device.type == DeviceType.GPU: |
| | assert device.id is not None |
| | return device.id |
| | else: |
| | return str(device) |
| |
|
| | if self._single_device is not None: |
| | return convert_device(self._single_device) |
| |
|
| | assert self._multiple_devices is not None |
| | return {key: convert_device(device, gpu_id_only=True) for key, device in self._multiple_devices.mapping.items()} |
| |
|
| | def update_hf_kwargs(self, hf_kwargs: Dict[str, Any], *, overwrite: bool) -> Dict[str, Any]: |
| | """ |
| | Convert the component device representation to HuggingFace format. |
| | |
| | Add them as canonical keyword arguments to the keyword arguments dictionary. |
| | |
| | :param hf_kwargs: |
| | The HuggingFace keyword arguments dictionary. |
| | :param overwrite: |
| | Whether to overwrite existing device arguments. |
| | :returns: |
| | The HuggingFace keyword arguments dictionary. |
| | """ |
| | self._validate() |
| |
|
| | if not overwrite and any(x in hf_kwargs for x in ("device", "device_map")): |
| | return hf_kwargs |
| |
|
| | converted = self.to_hf() |
| | key = "device_map" if self.has_multiple_devices else "device" |
| | hf_kwargs[key] = converted |
| | return hf_kwargs |
| |
|
| | @property |
| | def has_multiple_devices(self) -> bool: |
| | """ |
| | Whether this component device representation contains multiple devices. |
| | """ |
| | self._validate() |
| |
|
| | return self._multiple_devices is not None |
| |
|
| | @property |
| | def first_device(self) -> Optional["ComponentDevice"]: |
| | """ |
| | Return either the single device or the first device in the device map, if any. |
| | |
| | :returns: |
| | The first device. |
| | """ |
| | self._validate() |
| |
|
| | if self._single_device is not None: |
| | return self.from_single(self._single_device) |
| |
|
| | assert self._multiple_devices is not None |
| | assert self._multiple_devices.first_device is not None |
| | return self.from_single(self._multiple_devices.first_device) |
| |
|
| | @staticmethod |
| | def resolve_device(device: Optional["ComponentDevice"] = None) -> "ComponentDevice": |
| | """ |
| | Select a device for a component. If a device is specified, it's used. Otherwise, the default device is used. |
| | |
| | :param device: |
| | The provided device, if any. |
| | :returns: |
| | The resolved device. |
| | """ |
| | if not isinstance(device, ComponentDevice) and device is not None: |
| | raise ValueError( |
| | f"Invalid component device type '{type(device).__name__}'. Must either be None or ComponentDevice." |
| | ) |
| |
|
| | if device is None: |
| | device = ComponentDevice.from_single(_get_default_device()) |
| |
|
| | return device |
| |
|
| | def to_dict(self) -> Dict[str, Any]: |
| | """ |
| | Convert the component device representation to a JSON-serializable dictionary. |
| | |
| | :returns: |
| | The dictionary representation. |
| | """ |
| | if self._single_device is not None: |
| | return {"type": "single", "device": str(self._single_device)} |
| | elif self._multiple_devices is not None: |
| | return {"type": "multiple", "device_map": self._multiple_devices.to_dict()} |
| | else: |
| | |
| | assert False |
| |
|
| | @classmethod |
| | def from_dict(cls, dict: Dict[str, Any]) -> "ComponentDevice": |
| | """ |
| | Create a component device representation from a JSON-serialized dictionary. |
| | |
| | :param dict: |
| | The serialized representation. |
| | :returns: |
| | The deserialized component device. |
| | """ |
| | if dict["type"] == "single": |
| | return cls.from_str(dict["device"]) |
| | elif dict["type"] == "multiple": |
| | return cls.from_multiple(DeviceMap.from_dict(dict["device_map"])) |
| | else: |
| | raise ValueError(f"Unknown component device type '{dict['type']}' in serialized data") |
| |
|
| |
|
| | def _get_default_device() -> Device: |
| | """ |
| | Return the default device for Haystack. |
| | |
| | Precedence: |
| | GPU > MPS > CPU. If PyTorch is not installed, only CPU is available. |
| | |
| | :returns: |
| | The default device. |
| | """ |
| | try: |
| | torch_import.check() |
| |
|
| | has_mps = ( |
| | hasattr(torch.backends, "mps") |
| | and torch.backends.mps.is_available() |
| | and os.getenv("HAYSTACK_MPS_ENABLED", "true") != "false" |
| | ) |
| | has_cuda = torch.cuda.is_available() |
| | except ImportError: |
| | has_mps = False |
| | has_cuda = False |
| |
|
| | if has_cuda: |
| | return Device.gpu() |
| | elif has_mps: |
| | return Device.mps() |
| | else: |
| | return Device.cpu() |
| |
|
| |
|
| | def _split_device_string(string: str) -> Tuple[str, Optional[int]]: |
| | """ |
| | Split a device string into device type and device id. |
| | |
| | :param string: |
| | The device string to split. |
| | :returns: |
| | The device type and device id, if any. |
| | """ |
| | if ":" in string: |
| | device_type, device_id_str = string.split(":") |
| | try: |
| | device_id = int(device_id_str) |
| | except ValueError: |
| | raise ValueError(f"Device id must be an integer, got {device_id_str}") |
| | else: |
| | device_type = string |
| | device_id = None |
| | return device_type, device_id |
| |
|