Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

2026-09-24 13:22:23 +07:00
commit 642cc11a9f
18968 changed files with 5683248 additions and 0 deletions
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+import torch
+
+
+__all__ = [
+    "GreenContext",
+]
+
+_GreenContext = object
+SUPPORTED = False
+
+if hasattr(torch._C, "_CUDAGreenContext"):
+    _GreenContext = torch._C._CUDAGreenContext  # type: ignore[misc]
+    SUPPORTED = True
+
+
+# Python shim helps Sphinx process docstrings more reliably.
+# pyrefly: ignore [invalid-inheritance]
+class GreenContext(_GreenContext):
+    r"""Wrapper around a CUDA green context.
+
+    .. warning::
+       This API is in beta and may change in future releases.
+    """
+
+    @staticmethod
+    def create(
+        *,
+        num_sms: int | None = None,
+        workqueue_scope: str | None = None,
+        workqueue_concurrency_limit: int | None = None,
+        device_id: int | None = None,
+    ) -> _GreenContext:
+        r"""Create a CUDA green context.
+
+        At least one of ``num_sms`` or ``workqueue_scope`` must be specified.
+        Both can be combined to partition SMs and configure workqueues in the
+        same green context.
+
+        Arguments:
+            num_sms (int, optional): The number of SMs to use in the green
+                context. When ``None``, SMs are not partitioned.
+            workqueue_scope (str, optional): Workqueue sharing scope. One of
+                ``"device_ctx"`` (shared across all contexts, default driver
+                behaviour) or ``"balanced"`` (non-overlapping workqueues with
+                other balanced green contexts). When ``None``, no workqueue
+                configuration is applied.
+            workqueue_concurrency_limit (int, optional): Maximum number of
+                concurrent stream-ordered workloads for the workqueue. Requires
+                ``workqueue_scope`` to be set.
+            device_id (int, optional): The device index of green context.
+                When ``None``, the current device is used.
+        """
+        if not SUPPORTED:
+            raise RuntimeError("PyTorch was not built with Green Context support!")
+        return _GreenContext.create(  # type: ignore[attr-defined]
+            device_id=device_id,
+            num_sms=num_sms,
+            workqueue_scope=workqueue_scope,
+            workqueue_concurrency_limit=workqueue_concurrency_limit,
+        )
+
+    @staticmethod
+    def max_workqueue_concurrency(device_id: int | None = None) -> int:
+        r"""Return the maximum workqueue concurrency limit for the device.
+
+        This queries the device for the default number of concurrent
+        stream-ordered workloads supported by workqueue configuration
+        resources.
+
+        Arguments:
+            device_id (int, optional): The device index to query. When
+                ``None``, the current device is used.
+        """
+        if not SUPPORTED:
+            raise RuntimeError("PyTorch was not built with Green Context support!")
+        return _GreenContext.max_workqueue_concurrency(device_id=device_id)  # type: ignore[attr-defined]
+
+    # Note that these functions are bypassed but we define them here
+    # for Sphinx documentation purposes
+    def set_context(self) -> None:  # pylint: disable=useless-parent-delegation
+        r"""Make the green context the current context."""
+        return super().set_context()  # type: ignore[misc]
+
+    def pop_context(self) -> None:  # pylint: disable=useless-parent-delegation
+        r"""Assuming the green context is the current context, pop it from the
+        context stack and restore the previous context.
+        """
+        return super().pop_context()  # type: ignore[misc]
+
+    def Stream(self) -> "torch.cuda.Stream":
+        r"""Return the CUDA Stream used by the green context."""
+        return super().Stream()