450 lines
15 KiB
Python
450 lines
15 KiB
Python
# mypy: allow-untyped-defs
|
|
|
|
"""
|
|
Device abstraction layer for TorchDynamo and Inductor backends.
|
|
|
|
This module provides a unified interface for different hardware backends (CUDA, XPU,
|
|
CPU, MPS) through a common device interface. Key components include:
|
|
|
|
- DeviceInterface: Base class defining the common API for all device types
|
|
- Device-specific implementations: CudaInterface, XpuInterface, CpuInterface, MpsInterface
|
|
- Device registration system for managing available backends
|
|
- Worker APIs for multi-processing scenarios
|
|
- Stream and event management across different devices
|
|
- Device property caching for worker processes
|
|
|
|
The abstraction layer enables device-agnostic code in TorchDynamo while allowing
|
|
specialized implementations for each hardware backend's unique features.
|
|
"""
|
|
|
|
import time
|
|
from collections.abc import Iterable
|
|
from dataclasses import dataclass
|
|
from typing import Any, Callable, Optional, Union
|
|
|
|
import torch
|
|
|
|
|
|
get_cuda_stream: Optional[Callable[[int], int]]
|
|
if torch.cuda._is_compiled():
|
|
from torch._C import _cuda_getCurrentRawStream as get_cuda_stream
|
|
else:
|
|
get_cuda_stream = None
|
|
|
|
_device_t = Union[torch.device, str, int, None]
|
|
|
|
# Recording the device properties in the main process but used in worker process.
|
|
caching_worker_device_properties: dict[str, Any] = {}
|
|
caching_worker_current_devices: dict[str, int] = {}
|
|
|
|
|
|
class DeviceInterface:
|
|
"""
|
|
This is a simple device runtime interface for Inductor. It enables custom
|
|
backends to be integrated with Inductor in a device-agnostic semantic.
|
|
"""
|
|
|
|
class device:
|
|
def __new__(cls, device: _device_t):
|
|
raise NotImplementedError
|
|
|
|
class Event:
|
|
def __new__(cls, *args, **kwargs):
|
|
raise NotImplementedError(
|
|
"Event should be inherited from torch.Event, otherwise, it couldn't be captured by dynamo."
|
|
)
|
|
|
|
class Stream:
|
|
def __new__(cls, *args, **kwargs):
|
|
raise NotImplementedError(
|
|
"Stream should be inherited from torch.Stream, otherwise, it couldn't be captured by dynamo."
|
|
)
|
|
|
|
class Worker:
|
|
"""
|
|
Worker API to query device properties that will work in multi processing
|
|
workers that cannot use the GPU APIs (due to processing fork() and
|
|
initialization time issues). Properties are recorded in the main process
|
|
before we fork the workers.
|
|
"""
|
|
|
|
@staticmethod
|
|
def set_device(device: int):
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def current_device() -> int:
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def get_device_properties(device: _device_t = None):
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def current_device():
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def set_device(device: _device_t):
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def maybe_exchange_device(device: int) -> int:
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def exchange_device(device: int) -> int:
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def device_count():
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def is_available() -> bool:
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def stream(stream: torch.Stream):
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def current_stream():
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def set_stream(stream: torch.Stream):
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def _set_stream_by_id(stream_id: int, device_index: int, device_type: int):
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def get_raw_stream(device_idx: int) -> int:
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def synchronize(device: _device_t = None):
|
|
raise NotImplementedError
|
|
|
|
@classmethod
|
|
def get_device_properties(cls, device: _device_t = None):
|
|
return cls.Worker.get_device_properties(device)
|
|
|
|
@staticmethod
|
|
def get_compute_capability(device: _device_t = None):
|
|
raise NotImplementedError
|
|
|
|
@staticmethod
|
|
def is_bf16_supported(including_emulation: bool = False):
|
|
raise NotImplementedError
|
|
|
|
@classmethod
|
|
def is_dtype_supported(
|
|
cls, dtype: torch.dtype, including_emulation: bool = False
|
|
) -> bool:
|
|
return dtype != torch.bfloat16 or cls.is_bf16_supported(including_emulation)
|
|
|
|
@staticmethod
|
|
def memory_allocated(device: _device_t = None) -> int:
|
|
raise NotImplementedError
|
|
|
|
|
|
class DeviceGuard:
|
|
"""
|
|
This class provides a context manager for device switching. This is a stripped
|
|
down version of torch.{device_name}.device.
|
|
|
|
The context manager changes the current device to the given device index
|
|
on entering the context and restores the original device on exiting.
|
|
The device is switched using the provided device interface.
|
|
"""
|
|
|
|
def __init__(
|
|
self, device_interface: type[DeviceInterface], index: Optional[int]
|
|
) -> None:
|
|
self.device_interface = device_interface
|
|
self.idx = index
|
|
self.prev_idx = -1
|
|
|
|
def __enter__(self):
|
|
if self.idx is not None:
|
|
self.prev_idx = self.device_interface.exchange_device(self.idx)
|
|
|
|
def __exit__(self, type: Any, value: Any, traceback: Any):
|
|
if self.idx is not None:
|
|
self.idx = self.device_interface.maybe_exchange_device(self.prev_idx)
|
|
return False
|
|
|
|
|
|
class CudaInterface(DeviceInterface):
|
|
device = torch.cuda.device
|
|
|
|
# register Event and Stream class into the backend interface
|
|
# make sure Event and Stream are implemented and inherited from the torch.Event and torch.Stream
|
|
Event = torch.cuda.Event
|
|
Stream = torch.cuda.Stream
|
|
|
|
class Worker:
|
|
@staticmethod
|
|
def set_device(device: int):
|
|
caching_worker_current_devices["cuda"] = device
|
|
|
|
@staticmethod
|
|
def current_device() -> int:
|
|
if "cuda" in caching_worker_current_devices:
|
|
return caching_worker_current_devices["cuda"]
|
|
return torch.cuda.current_device()
|
|
|
|
@staticmethod
|
|
def get_device_properties(device: _device_t = None):
|
|
if device is not None:
|
|
if isinstance(device, str):
|
|
device = torch.device(device)
|
|
assert device.type == "cuda"
|
|
if isinstance(device, torch.device):
|
|
device = device.index
|
|
if device is None:
|
|
device = CudaInterface.Worker.current_device()
|
|
|
|
if "cuda" not in caching_worker_device_properties:
|
|
device_prop = [
|
|
torch.cuda.get_device_properties(i)
|
|
for i in range(torch.cuda.device_count())
|
|
]
|
|
caching_worker_device_properties["cuda"] = device_prop
|
|
|
|
return caching_worker_device_properties["cuda"][device]
|
|
|
|
current_device = staticmethod(torch.cuda.current_device)
|
|
set_device = staticmethod(torch.cuda.set_device)
|
|
device_count = staticmethod(torch.cuda.device_count)
|
|
stream = staticmethod(torch.cuda.stream) # type: ignore[assignment]
|
|
current_stream = staticmethod(torch.cuda.current_stream)
|
|
set_stream = staticmethod(torch.cuda.set_stream) # type: ignore[assignment]
|
|
_set_stream_by_id = staticmethod(torch.cuda._set_stream_by_id) # type: ignore[assignment]
|
|
synchronize = staticmethod(torch.cuda.synchronize)
|
|
get_device_properties = staticmethod(torch.cuda.get_device_properties) # type: ignore[assignment]
|
|
get_raw_stream = staticmethod(get_cuda_stream) # type: ignore[assignment, arg-type]
|
|
exchange_device = staticmethod(torch.cuda._exchange_device) # type: ignore[arg-type]
|
|
maybe_exchange_device = staticmethod(torch.cuda._maybe_exchange_device) # type: ignore[arg-type]
|
|
memory_allocated = staticmethod(torch.cuda.memory_allocated)
|
|
is_bf16_supported = staticmethod(torch.cuda.is_bf16_supported) # type: ignore[arg-type]
|
|
|
|
# Can be mock patched by @patch decorator.
|
|
@staticmethod
|
|
def is_available() -> bool:
|
|
return torch.cuda.is_available()
|
|
|
|
@staticmethod
|
|
def get_compute_capability(device: _device_t = None):
|
|
if torch.version.hip is None:
|
|
major, min = torch.cuda.get_device_capability(device)
|
|
return major * 10 + min
|
|
else:
|
|
return torch.cuda.get_device_properties(device).gcnArchName.split(":", 1)[0]
|
|
|
|
|
|
get_xpu_stream: Optional[Callable[[int], int]]
|
|
if torch.xpu._is_compiled():
|
|
from torch._C import _xpu_getCurrentRawStream as get_xpu_stream
|
|
else:
|
|
get_xpu_stream = None
|
|
|
|
|
|
class XpuInterface(DeviceInterface):
|
|
device = torch.xpu.device
|
|
Event = torch.xpu.Event
|
|
Stream = torch.xpu.Stream
|
|
|
|
class Worker:
|
|
@staticmethod
|
|
def set_device(device: int):
|
|
caching_worker_current_devices["xpu"] = device
|
|
|
|
@staticmethod
|
|
def current_device() -> int:
|
|
if "xpu" in caching_worker_current_devices:
|
|
return caching_worker_current_devices["xpu"]
|
|
return torch.xpu.current_device()
|
|
|
|
@staticmethod
|
|
def get_device_properties(device: _device_t = None):
|
|
if device is not None:
|
|
if isinstance(device, str):
|
|
device = torch.device(device)
|
|
assert device.type == "xpu"
|
|
if isinstance(device, torch.device):
|
|
device = device.index
|
|
if device is None:
|
|
device = XpuInterface.Worker.current_device()
|
|
|
|
if "xpu" not in caching_worker_device_properties:
|
|
device_prop = [
|
|
torch.xpu.get_device_properties(i)
|
|
for i in range(torch.xpu.device_count())
|
|
]
|
|
caching_worker_device_properties["xpu"] = device_prop
|
|
|
|
return caching_worker_device_properties["xpu"][device]
|
|
|
|
current_device = staticmethod(torch.xpu.current_device)
|
|
set_device = staticmethod(torch.xpu.set_device)
|
|
device_count = staticmethod(torch.xpu.device_count)
|
|
stream = staticmethod(torch.xpu.stream) # type: ignore[assignment]
|
|
current_stream = staticmethod(torch.xpu.current_stream)
|
|
set_stream = staticmethod(torch.xpu.set_stream) # type: ignore[assignment]
|
|
_set_stream_by_id = staticmethod(torch.xpu._set_stream_by_id) # type: ignore[assignment]
|
|
synchronize = staticmethod(torch.xpu.synchronize)
|
|
get_device_properties = staticmethod(torch.xpu.get_device_properties) # type: ignore[assignment]
|
|
get_raw_stream = staticmethod(get_xpu_stream) # type: ignore[assignment, arg-type]
|
|
exchange_device = staticmethod(torch.xpu._exchange_device) # type: ignore[arg-type]
|
|
maybe_exchange_device = staticmethod(torch.xpu._maybe_exchange_device) # type: ignore[arg-type]
|
|
memory_allocated = staticmethod(torch.xpu.memory_allocated)
|
|
|
|
# Can be mock patched by @patch decorator.
|
|
@staticmethod
|
|
def is_available() -> bool:
|
|
return torch.xpu.is_available()
|
|
|
|
@staticmethod
|
|
def get_compute_capability(device: _device_t = None):
|
|
cc = torch.xpu.get_device_capability(device)
|
|
return cc
|
|
|
|
@staticmethod
|
|
def is_bf16_supported(including_emulation: bool = False) -> bool:
|
|
return torch.xpu.is_bf16_supported()
|
|
|
|
|
|
@dataclass
|
|
class CpuDeviceProperties:
|
|
multi_processor_count: int
|
|
|
|
|
|
class CpuInterface(DeviceInterface):
|
|
class Event(torch.Event):
|
|
def __init__(self, enable_timing=True):
|
|
self.time = 0.0
|
|
|
|
def elapsed_time(self, end_event) -> float:
|
|
return (end_event.time - self.time) * 1000
|
|
|
|
def record(self, stream=None):
|
|
self.time = time.perf_counter()
|
|
|
|
@staticmethod
|
|
def is_available() -> bool:
|
|
return True
|
|
|
|
@staticmethod
|
|
def is_bf16_supported(including_emulation: bool = False):
|
|
return True
|
|
|
|
@staticmethod
|
|
def get_compute_capability(device: _device_t = None) -> str:
|
|
return ""
|
|
|
|
@staticmethod
|
|
def get_raw_stream(device_idx) -> int:
|
|
return 0
|
|
|
|
@staticmethod
|
|
def current_device():
|
|
return 0
|
|
|
|
@staticmethod
|
|
def synchronize(device: _device_t = None):
|
|
pass
|
|
|
|
class Worker:
|
|
@staticmethod
|
|
def get_device_properties(device: _device_t = None):
|
|
import multiprocessing
|
|
|
|
cpu_count = multiprocessing.cpu_count()
|
|
return CpuDeviceProperties(cpu_count)
|
|
|
|
|
|
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 == torch.float64:
|
|
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():
|
|
return 0
|
|
|
|
@staticmethod
|
|
def get_compute_capability(device: _device_t = None) -> str:
|
|
return ""
|
|
|
|
@staticmethod
|
|
def synchronize(device: _device_t = None):
|
|
torch.mps.synchronize()
|
|
|
|
class Worker:
|
|
@staticmethod
|
|
def get_device_properties(device: _device_t = None):
|
|
return {}
|
|
|
|
@staticmethod
|
|
def current_device():
|
|
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]
|
|
):
|
|
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():
|
|
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("cpu", CpuInterface)
|
|
register_interface_for_device("mps", MpsInterface)
|
|
|
|
_device_initialized = True
|