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c855f8632e
Adds suppressions to pyrefly will typecheck clean: https://github.com/pytorch/pytorch/issues/163283 Almost there! Test plan: dmypy restart && python3 scripts/lintrunner.py -a pyrefly check step 1: delete lines in the pyrefly.toml file from the project-excludes field step 2: run pyrefly check step 3: add suppressions, clean up unused suppressions before: https://gist.github.com/maggiemoss/4b3bf2037014e116bc00706a16aef199 after: INFO 0 errors (6,884 ignored) Pull Request resolved: https://github.com/pytorch/pytorch/pull/164913 Approved by: https://github.com/oulgen
613 lines
22 KiB
Python
613 lines
22 KiB
Python
"""
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Device abstraction layer for TorchDynamo and Inductor backends.
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This module provides a unified interface for different hardware backends (CUDA, XPU,
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CPU, MPS, MTIA) through a common device interface. Key components include:
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- DeviceInterface: Base class defining the common API for all device types
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- Device-specific implementations: CudaInterface, XpuInterface, CpuInterface, MpsInterface, MtiaInterface
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- Device registration system for managing available backends
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- Worker APIs for multi-processing scenarios
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- Stream and event management across different devices
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- Device property caching for worker processes
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The abstraction layer enables device-agnostic code in TorchDynamo while allowing
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specialized implementations for each hardware backend's unique features.
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"""
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import inspect
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import time
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from collections import namedtuple
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from collections.abc import Iterable
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from dataclasses import dataclass
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from typing import Any, Callable, Literal, Optional, Union
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import torch
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get_cuda_stream: Optional[Callable[[int], int]]
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if torch.cuda._is_compiled():
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from torch._C import _cuda_getCurrentRawStream as get_cuda_stream
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else:
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get_cuda_stream = None
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# Recording the device properties in the main process but used in worker process.
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caching_worker_device_properties: dict[str, Any] = {}
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caching_worker_current_devices: dict[str, int] = {}
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class DeviceInterface:
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"""
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This is a simple device runtime interface for Inductor. It enables custom
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backends to be integrated with Inductor in a device-agnostic semantic.
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"""
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class device:
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def __new__(cls, device: torch.types.Device) -> Any:
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raise NotImplementedError
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class Event:
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def __new__(cls, *args: Any, **kwargs: Any) -> Any:
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raise NotImplementedError(
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"Event should be inherited from torch.Event, otherwise, it couldn't be captured by dynamo."
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)
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class Stream:
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def __new__(cls, *args: Any, **kwargs: Any) -> Any:
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raise NotImplementedError(
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"Stream should be inherited from torch.Stream, otherwise, it couldn't be captured by dynamo."
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)
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class Worker:
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"""
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Worker API to query device properties that will work in multi processing
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workers that cannot use the GPU APIs (due to processing fork() and
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initialization time issues). Properties are recorded in the main process
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before we fork the workers.
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"""
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@staticmethod
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def set_device(device: int) -> None:
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raise NotImplementedError
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@staticmethod
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def current_device() -> int:
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raise NotImplementedError
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@staticmethod
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def get_device_properties(device: torch.types.Device = None) -> Any:
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raise NotImplementedError
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@staticmethod
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def current_device() -> int:
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raise NotImplementedError
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@staticmethod
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def set_device(device: torch.types.Device) -> None:
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raise NotImplementedError
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@staticmethod
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def maybe_exchange_device(device: int) -> int:
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raise NotImplementedError
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@staticmethod
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def exchange_device(device: int) -> int:
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raise NotImplementedError
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@staticmethod
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def device_count() -> int:
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raise NotImplementedError
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@staticmethod
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def is_available() -> bool:
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raise NotImplementedError
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@staticmethod
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def stream(stream: torch.Stream) -> Any:
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raise NotImplementedError
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@staticmethod
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def current_stream() -> torch.Stream:
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raise NotImplementedError
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@staticmethod
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def set_stream(stream: torch.Stream) -> None:
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raise NotImplementedError
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@staticmethod
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def _set_stream_by_id(stream_id: int, device_index: int, device_type: int) -> None:
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raise NotImplementedError
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@staticmethod
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def get_raw_stream(device_idx: int) -> int:
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raise NotImplementedError
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@staticmethod
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def synchronize(device: torch.types.Device = None) -> None:
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raise NotImplementedError
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@classmethod
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def get_device_properties(cls, device: torch.types.Device = None) -> Any:
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return cls.Worker.get_device_properties(device)
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@staticmethod
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def get_compute_capability(device: torch.types.Device = None) -> Any:
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raise NotImplementedError
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@staticmethod
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def is_bf16_supported(including_emulation: bool = False) -> bool:
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raise NotImplementedError
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@classmethod
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def is_dtype_supported(
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cls, dtype: torch.dtype, including_emulation: bool = False
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) -> bool:
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return dtype != torch.bfloat16 or cls.is_bf16_supported(including_emulation)
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@staticmethod
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def memory_allocated(device: torch.types.Device = None) -> int:
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raise NotImplementedError
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@staticmethod
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def is_triton_capable(device: torch.types.Device = None) -> bool:
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"""
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Returns True if the device has Triton support, False otherwise, even if
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the appropriate Triton backend is not available.
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"""
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return False
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@classmethod
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def raise_if_triton_unavailable(cls, device: torch.types.Device = None) -> None:
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"""
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Raises a `RuntimeError` with the appropriate human-readable instructions
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to resolve the issue if Triton is not available for the given device, or
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the default device if `device` is `None`.
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The caller should ensure the presence of the 'triton' package before
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calling this method.
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"""
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if not cls.is_triton_capable():
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raise RuntimeError("This device is not capable of supporting Triton")
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class DeviceGuard:
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"""
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This class provides a context manager for device switching. This is a stripped
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down version of torch.{device_name}.device.
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The context manager changes the current device to the given device index
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on entering the context and restores the original device on exiting.
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The device is switched using the provided device interface.
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"""
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def __init__(
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self, device_interface: type[DeviceInterface], index: Optional[int]
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) -> None:
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self.device_interface = device_interface
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self.idx = index
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self.prev_idx = -1
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def __enter__(self) -> None:
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if self.idx is not None:
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self.prev_idx = self.device_interface.exchange_device(self.idx)
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def __exit__(self, type: Any, value: Any, traceback: Any) -> Literal[False]:
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if self.idx is not None:
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self.idx = self.device_interface.maybe_exchange_device(self.prev_idx)
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return False
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class CudaInterface(DeviceInterface):
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device = torch.cuda.device # type: ignore[assignment]
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# register Event and Stream class into the backend interface
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# make sure Event and Stream are implemented and inherited from the torch.Event and torch.Stream
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Event = torch.cuda.Event # type: ignore[assignment]
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Stream = torch.cuda.Stream # type: ignore[assignment]
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# pyrefly: ignore # bad-override
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class Worker:
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@staticmethod
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def set_device(device: int) -> None:
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caching_worker_current_devices["cuda"] = device
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@staticmethod
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def current_device() -> int:
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if "cuda" in caching_worker_current_devices:
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return caching_worker_current_devices["cuda"]
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return torch.cuda.current_device()
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@staticmethod
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def get_device_properties(device: torch.types.Device = None) -> Any:
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if device is not None:
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if isinstance(device, str):
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device = torch.device(device)
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assert device.type == "cuda"
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if isinstance(device, torch.device):
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device = device.index
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if device is None:
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device = CudaInterface.Worker.current_device()
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if "cuda" not in caching_worker_device_properties:
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device_prop = [
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torch.cuda.get_device_properties(i)
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for i in range(torch.cuda.device_count())
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]
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caching_worker_device_properties["cuda"] = device_prop
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return caching_worker_device_properties["cuda"][device]
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current_device = staticmethod(torch.cuda.current_device)
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set_device = staticmethod(torch.cuda.set_device)
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device_count = staticmethod(torch.cuda.device_count)
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stream = staticmethod(torch.cuda.stream) # type: ignore[assignment]
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# pyrefly: ignore # bad-override
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current_stream = staticmethod(torch.cuda.current_stream)
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set_stream = staticmethod(torch.cuda.set_stream) # type: ignore[assignment]
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_set_stream_by_id = staticmethod(torch.cuda._set_stream_by_id) # type: ignore[assignment]
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synchronize = staticmethod(torch.cuda.synchronize)
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get_device_properties = staticmethod(torch.cuda.get_device_properties) # type: ignore[assignment]
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get_raw_stream = staticmethod(get_cuda_stream) # type: ignore[assignment, arg-type]
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exchange_device = staticmethod(torch.cuda._exchange_device) # type: ignore[arg-type, has-type]
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maybe_exchange_device = staticmethod(torch.cuda._maybe_exchange_device) # type: ignore[arg-type, has-type]
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memory_allocated = staticmethod(torch.cuda.memory_allocated)
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is_bf16_supported = staticmethod(torch.cuda.is_bf16_supported) # type: ignore[arg-type]
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# Can be mock patched by @patch decorator.
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@staticmethod
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def is_available() -> bool:
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return torch.cuda.is_available()
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@staticmethod
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def get_compute_capability(device: torch.types.Device = None) -> Union[int, str]:
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if torch.version.hip is None:
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major, min = torch.cuda.get_device_capability(device)
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return major * 10 + min
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else:
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return torch.cuda.get_device_properties(device).gcnArchName.split(":", 1)[0]
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@staticmethod
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def is_triton_capable(device: torch.types.Device = None) -> bool:
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return (
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torch.version.hip is not None
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or torch.cuda.get_device_properties(device).major >= 7
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)
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@staticmethod
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def raise_if_triton_unavailable(device: torch.types.Device = None) -> None:
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from torch._inductor.exc import GPUTooOldForTriton
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if not CudaInterface.is_triton_capable(device):
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device_props = torch.cuda.get_device_properties(device)
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raise GPUTooOldForTriton(device_props, inspect.currentframe())
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import triton.backends
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if torch.version.hip is not None:
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if "amd" not in triton.backends.backends:
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raise RuntimeError("triton not built with the 'amd' backend")
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elif "nvidia" not in triton.backends.backends:
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raise RuntimeError("triton not built with the 'nvidia' backend")
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get_mtia_stream: Optional[Callable[[int], int]]
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if torch.mtia._is_compiled():
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from torch._C import _mtia_getCurrentRawStream as get_mtia_stream
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else:
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get_mtia_stream = None
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class MtiaInterface(DeviceInterface):
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device = torch.mtia.device # type: ignore[assignment]
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Event = torch.mtia.Event # type: ignore[assignment]
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Stream = torch.mtia.Stream # type: ignore[assignment]
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# pyrefly: ignore # bad-override
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class Worker:
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@staticmethod
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def set_device(device: int) -> None:
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caching_worker_current_devices["mtia"] = device
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@staticmethod
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def current_device() -> int:
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if "mtia" in caching_worker_current_devices:
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return caching_worker_current_devices["mtia"]
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return torch.mtia.current_device()
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@staticmethod
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def get_device_properties(device: torch.types.Device = None) -> Any:
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if device is not None:
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if isinstance(device, str):
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device = torch.device(device)
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assert device.type == "mtia"
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if isinstance(device, torch.device):
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device = device.index
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if device is None:
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device = MtiaInterface.Worker.current_device()
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if "mtia" not in caching_worker_device_properties:
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device_prop = [
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torch.mtia.get_device_properties(i)
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for i in range(torch.mtia.device_count())
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]
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caching_worker_device_properties["mtia"] = device_prop
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return caching_worker_device_properties["mtia"][device]
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current_device = staticmethod(torch.mtia.current_device)
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set_device = staticmethod(torch.mtia.set_device) # type: ignore[assignment]
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device_count = staticmethod(torch.mtia.device_count)
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stream = staticmethod(torch.mtia.stream) # type: ignore[assignment]
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# pyrefly: ignore # bad-override
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current_stream = staticmethod(torch.mtia.current_stream)
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set_stream = staticmethod(torch.mtia.set_stream) # type: ignore[assignment]
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_set_stream_by_id = staticmethod(torch.mtia._set_stream_by_id) # type: ignore[assignment]
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synchronize = staticmethod(torch.mtia.synchronize)
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get_device_properties = staticmethod(torch.mtia.get_device_properties) # type: ignore[assignment]
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get_raw_stream = staticmethod(get_mtia_stream) # type: ignore[assignment, arg-type]
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exchange_device = staticmethod(torch.mtia._exchange_device) # type: ignore[arg-type, has-type]
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maybe_exchange_device = staticmethod(torch.mtia._maybe_exchange_device) # type: ignore[arg-type, has-type]
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memory_allocated = staticmethod(torch.mtia.memory_allocated) # type: ignore[assignment]
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is_bf16_supported = staticmethod(torch.mtia.is_bf16_supported) # type: ignore[arg-type]
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# Can be mock patched by @patch decorator.
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@staticmethod
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def is_available() -> bool:
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ret = torch.mtia.is_available()
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return ret
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@staticmethod
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def get_compute_capability(device: torch.types.Device = None) -> Any:
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cc = torch.mtia.get_device_capability(device)
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return cc
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@staticmethod
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def is_triton_capable(device: torch.types.Device = None) -> bool:
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return True
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@staticmethod
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def raise_if_triton_unavailable(evice: torch.types.Device = None) -> None:
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import triton.backends
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if "mtia" not in triton.backends.backends:
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raise RuntimeError("triton not built with the 'mtia' backend")
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get_xpu_stream: Optional[Callable[[int], int]]
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if torch.xpu._is_compiled():
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from torch._C import _xpu_getCurrentRawStream as get_xpu_stream
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else:
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get_xpu_stream = None
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class XpuInterface(DeviceInterface):
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device = torch.xpu.device # type: ignore[assignment]
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Event = torch.xpu.Event # type: ignore[assignment]
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Stream = torch.xpu.Stream # type: ignore[assignment]
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# pyrefly: ignore # bad-override
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class Worker:
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@staticmethod
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def set_device(device: int) -> None:
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caching_worker_current_devices["xpu"] = device
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@staticmethod
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def current_device() -> int:
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if "xpu" in caching_worker_current_devices:
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return caching_worker_current_devices["xpu"]
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return torch.xpu.current_device()
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@staticmethod
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def get_device_properties(device: torch.types.Device = None) -> Any:
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if device is not None:
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if isinstance(device, str):
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device = torch.device(device)
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assert device.type == "xpu"
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if isinstance(device, torch.device):
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device = device.index
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if device is None:
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device = XpuInterface.Worker.current_device()
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if "xpu" not in caching_worker_device_properties:
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device_prop = [
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torch.xpu.get_device_properties(i)
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for i in range(torch.xpu.device_count())
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]
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caching_worker_device_properties["xpu"] = device_prop
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return caching_worker_device_properties["xpu"][device]
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current_device = staticmethod(torch.xpu.current_device)
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set_device = staticmethod(torch.xpu.set_device)
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device_count = staticmethod(torch.xpu.device_count) # type: ignore[has-type]
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stream = staticmethod(torch.xpu.stream) # type: ignore[assignment]
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# pyrefly: ignore # bad-override
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current_stream = staticmethod(torch.xpu.current_stream)
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set_stream = staticmethod(torch.xpu.set_stream) # type: ignore[assignment]
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_set_stream_by_id = staticmethod(torch.xpu._set_stream_by_id) # type: ignore[assignment]
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synchronize = staticmethod(torch.xpu.synchronize)
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get_device_properties = staticmethod(torch.xpu.get_device_properties) # type: ignore[assignment]
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get_raw_stream = staticmethod(get_xpu_stream) # type: ignore[assignment, arg-type]
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exchange_device = staticmethod(torch.xpu._exchange_device) # type: ignore[arg-type, has-type]
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maybe_exchange_device = staticmethod(torch.xpu._maybe_exchange_device) # type: ignore[arg-type, has-type]
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memory_allocated = staticmethod(torch.xpu.memory_allocated)
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# Can be mock patched by @patch decorator.
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@staticmethod
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def is_available() -> bool:
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return torch.xpu.is_available()
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@staticmethod
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def get_compute_capability(device: torch.types.Device = None) -> Any:
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cc = torch.xpu.get_device_capability(device)
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return cc
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@staticmethod
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def is_bf16_supported(including_emulation: bool = False) -> bool:
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return torch.xpu.is_bf16_supported()
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@staticmethod
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def is_triton_capable(device: torch.types.Device = None) -> bool:
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return True
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@staticmethod
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def raise_if_triton_unavailable(device: torch.types.Device = None) -> None:
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import triton.backends
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if "intel" not in triton.backends.backends:
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raise RuntimeError("triton not built with the 'intel' backend")
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@dataclass
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class CpuDeviceProperties:
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multi_processor_count: int
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class CpuInterface(DeviceInterface):
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# pyrefly: ignore # bad-override
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class Event(torch.Event):
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def __init__(self, enable_timing: bool = True) -> None:
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self.time = 0.0
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def elapsed_time(self, end_event: Any) -> float:
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return (end_event.time - self.time) * 1000
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def record(self, stream: Any = None) -> None:
|
|
self.time = time.perf_counter()
|
|
|
|
# pyrefly: ignore # bad-override
|
|
class Worker:
|
|
@staticmethod
|
|
def get_device_properties(
|
|
device: torch.types.Device = None,
|
|
) -> CpuDeviceProperties:
|
|
import multiprocessing
|
|
|
|
cpu_count = multiprocessing.cpu_count()
|
|
return CpuDeviceProperties(cpu_count)
|
|
|
|
@staticmethod
|
|
def is_available() -> bool:
|
|
return True
|
|
|
|
@staticmethod
|
|
def is_bf16_supported(including_emulation: bool = False) -> bool:
|
|
return True
|
|
|
|
@staticmethod
|
|
def get_compute_capability(device: torch.types.Device = None) -> str:
|
|
return ""
|
|
|
|
@staticmethod
|
|
def get_raw_stream(device_idx: Any) -> int:
|
|
return 0
|
|
|
|
@staticmethod
|
|
def current_device() -> int:
|
|
return 0
|
|
|
|
@staticmethod
|
|
def synchronize(device: torch.types.Device = None) -> None:
|
|
pass
|
|
|
|
@staticmethod
|
|
def is_triton_capable(device: torch.types.Device = None) -> bool:
|
|
return True
|
|
|
|
@staticmethod
|
|
def raise_if_triton_unavailable(device: torch.types.Device = None) -> None:
|
|
import triton.backends
|
|
|
|
if "cpu" not in triton.backends.backends:
|
|
raise RuntimeError("triton not built with the 'cpu' backend")
|
|
|
|
|
|
class MpsInterface(DeviceInterface):
|
|
@staticmethod
|
|
def is_bf16_supported(including_emulation: bool = False) -> bool:
|
|
return torch.backends.mps.is_macos_or_newer(14, 0)
|
|
|
|
@classmethod
|
|
def is_dtype_supported(
|
|
cls, dtype: torch.dtype, including_emulation: bool = False
|
|
) -> bool:
|
|
if dtype in [torch.float64, torch.complex128]:
|
|
return False
|
|
return dtype != torch.bfloat16 or cls.is_bf16_supported(including_emulation)
|
|
|
|
@staticmethod
|
|
def is_available() -> bool:
|
|
return torch.backends.mps.is_available()
|
|
|
|
@staticmethod
|
|
def current_device() -> int:
|
|
return 0
|
|
|
|
@staticmethod
|
|
def get_compute_capability(device: torch.types.Device = None) -> str:
|
|
return ""
|
|
|
|
@staticmethod
|
|
def synchronize(device: torch.types.Device = None) -> None:
|
|
torch.mps.synchronize()
|
|
|
|
# pyrefly: ignore # bad-override
|
|
class Worker:
|
|
@staticmethod
|
|
def get_device_properties(device: torch.types.Device = None) -> Any:
|
|
return namedtuple("MPSProperties", ["multi_processor_count"])(
|
|
torch.backends.mps.get_core_count() # type: ignore[arg-type]
|
|
)
|
|
|
|
@staticmethod
|
|
def current_device() -> int:
|
|
return 0
|
|
|
|
|
|
device_interfaces: dict[str, type[DeviceInterface]] = {}
|
|
_device_initialized = False
|
|
|
|
|
|
def register_interface_for_device(
|
|
device: Union[str, torch.device], device_interface: type[DeviceInterface]
|
|
) -> None:
|
|
if isinstance(device, torch.device):
|
|
device = device.type
|
|
device_interfaces[device] = device_interface
|
|
|
|
|
|
def get_interface_for_device(device: Union[str, torch.device]) -> type[DeviceInterface]:
|
|
if isinstance(device, torch.device):
|
|
device = device.type
|
|
if not _device_initialized:
|
|
init_device_reg()
|
|
if device in device_interfaces:
|
|
return device_interfaces[device]
|
|
raise NotImplementedError(f"No interface for device {device}")
|
|
|
|
|
|
def get_registered_device_interfaces() -> Iterable[tuple[str, type[DeviceInterface]]]:
|
|
if not _device_initialized:
|
|
init_device_reg()
|
|
return device_interfaces.items()
|
|
|
|
|
|
def init_device_reg() -> None:
|
|
global _device_initialized
|
|
register_interface_for_device("cuda", CudaInterface)
|
|
for i in range(torch.cuda.device_count()):
|
|
register_interface_for_device(f"cuda:{i}", CudaInterface)
|
|
|
|
register_interface_for_device("xpu", XpuInterface)
|
|
for i in range(torch.xpu.device_count()):
|
|
register_interface_for_device(f"xpu:{i}", XpuInterface)
|
|
|
|
register_interface_for_device("mtia", MtiaInterface)
|
|
for i in range(torch.mtia.device_count()):
|
|
register_interface_for_device(f"mtia:{i}", MtiaInterface)
|
|
|
|
register_interface_for_device("cpu", CpuInterface)
|
|
register_interface_for_device("mps", MpsInterface)
|
|
|
|
_device_initialized = True
|