Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets
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Kolp
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import functools
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import hashlib
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import os
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from typing import Any
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@functools.cache
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def has_triton_package() -> bool:
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try:
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import triton # noqa: F401
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return True
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except ImportError:
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return False
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@functools.cache
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def get_triton_version(fallback: tuple[int, int] = (0, 0)) -> tuple[int, int]:
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try:
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import triton
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major, minor = tuple(int(v) for v in triton.__version__.split(".")[:2])
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return (major, minor)
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except ImportError:
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return fallback
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@functools.cache
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def _device_supports_tma() -> bool:
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import torch
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return (
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torch.cuda.is_available()
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and torch.cuda.get_device_capability() >= (9, 0)
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and not torch.version.hip
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)
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@functools.cache
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def has_triton_experimental_host_tma() -> bool:
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if has_triton_package():
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if _device_supports_tma():
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try:
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from triton.tools.experimental_descriptor import ( # noqa: F401
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create_1d_tma_descriptor,
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create_2d_tma_descriptor,
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)
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try:
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from triton.tools.experimental_descriptor import enable_in_pytorch
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return enable_in_pytorch()
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except ImportError:
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return True
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except ImportError:
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pass
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return False
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@functools.cache
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def has_triton_tensor_descriptor_host_tma() -> bool:
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if has_triton_package():
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if _device_supports_tma():
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try:
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from triton.tools.tensor_descriptor import ( # noqa: F401
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TensorDescriptor,
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)
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return True
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except ImportError:
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pass
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return False
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@functools.cache
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def has_triton_tma() -> bool:
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return has_triton_tensor_descriptor_host_tma() or has_triton_experimental_host_tma()
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@functools.cache
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def has_triton_tma_device() -> bool:
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if has_triton_package():
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import torch
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if (
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torch.cuda.is_available()
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and torch.cuda.get_device_capability() >= (9, 0)
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and not torch.version.hip
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) or torch.xpu.is_available():
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# old API
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try:
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from triton.language.extra.cuda import ( # noqa: F401
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experimental_device_tensormap_create1d,
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experimental_device_tensormap_create2d,
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)
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return True
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except ImportError:
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pass
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# new API
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try:
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from triton.language import make_tensor_descriptor # noqa: F401
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return True
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except ImportError:
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pass
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return False
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@functools.cache
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def has_datacenter_blackwell_tma_device() -> bool:
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import torch
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if (
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torch.cuda.is_available()
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and torch.cuda.get_device_capability() >= (10, 0)
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and torch.cuda.get_device_capability() < (11, 0)
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and not torch.version.hip
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):
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return has_triton_tma_device() and has_triton_tensor_descriptor_host_tma()
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return False
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@functools.lru_cache(None)
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def has_triton_stable_tma_api() -> bool:
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if has_triton_package():
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import torch
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if (
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torch.cuda.is_available()
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and torch.cuda.get_device_capability() >= (9, 0)
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and not torch.version.hip
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) or torch.xpu.is_available():
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try:
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from triton.language import make_tensor_descriptor # noqa: F401
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return True
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except ImportError:
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pass
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return False
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@functools.cache
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def has_triton() -> bool:
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if not has_triton_package():
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return False
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from torch._inductor.config import triton_disable_device_detection
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if triton_disable_device_detection:
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return False
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from torch._dynamo.device_interface import get_interface_for_device
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def cuda_extra_check(device_interface: Any) -> bool:
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return device_interface.Worker.get_device_properties().major >= 7
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def cpu_extra_check(device_interface: Any) -> bool:
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import triton.backends
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return "cpu" in triton.backends.backends
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def _return_true(device_interface: Any) -> bool:
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return True
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triton_supported_devices = {
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"cuda": cuda_extra_check,
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"xpu": _return_true,
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"cpu": cpu_extra_check,
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"mtia": _return_true,
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}
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def is_device_compatible_with_triton() -> bool:
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for device, extra_check in triton_supported_devices.items():
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device_interface = get_interface_for_device(device)
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if device_interface.is_available() and extra_check(device_interface):
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return True
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return False
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return is_device_compatible_with_triton()
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@functools.cache
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def triton_backend() -> Any:
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from triton.compiler.compiler import make_backend
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from triton.runtime.driver import driver
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target = driver.active.get_current_target()
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return make_backend(target)
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def _extern_libs_key(backend: Any) -> str:
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"""Return a cache key fragment for extern libs (e.g. libdevice.10.bc).
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These files affect codegen but are not covered by triton_key() (Python
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sources only) or backend.hash() (ptxas version and arch only).
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"""
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opts = backend.parse_options({})
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extern_libs = getattr(opts, "extern_libs", None)
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if not extern_libs:
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return ""
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parts = []
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for name, path in sorted(extern_libs):
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if os.path.isfile(path):
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with open(path, "rb") as f:
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parts.append(f"{name}-{hashlib.sha256(f.read()).hexdigest()}")
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return "-".join(parts)
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@functools.cache
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def triton_hash_with_backend() -> str:
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from torch._inductor.runtime.triton_compat import triton_key
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backend = triton_backend()
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key = f"{triton_key()}-{backend.hash()}"
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# Hash is upper case so that it can't contain any Python keywords.
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return hashlib.sha256(key.encode("utf-8")).hexdigest().upper()
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