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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+from enum import Enum
+
+import torch
+
+
+class EffectType(Enum):
+    ORDERED = "Ordered"
+
+
+from torch._library.utils import RegistrationHandle
+
+
+# These classes do not have side effects as they just store quantization
+# params, so we dont need to mark them as ordered
+skip_classes = (
+    "__torch__.torch.classes.quantized.Conv2dPackedParamsBase",
+    "__torch__.torch.classes.quantized.Conv3dPackedParamsBase",
+    "__torch__.torch.classes.quantized.EmbeddingPackedParamsBase",
+    "__torch__.torch.classes.quantized.LinearPackedParamsBase",
+    "__torch__.torch.classes.xnnpack.Conv2dOpContext",
+    "__torch__.torch.classes.xnnpack.LinearOpContext",
+    "__torch__.torch.classes.xnnpack.TransposeConv2dOpContext",
+)
+
+
+class EffectHolder:
+    """A holder where one can register an effect impl to."""
+
+    def __init__(self, qualname: str):
+        self.qualname: str = qualname
+        self._set_default_effect()
+
+    def _set_default_effect(self) -> None:
+        self._effect: EffectType | None = None
+
+        # If the op contains a ScriptObject input, we want to mark it as having effects
+        namespace, opname = torch._library.utils.parse_namespace(self.qualname)
+        split = opname.split(".")
+        if len(split) > 1:
+            if len(split) != 2:
+                raise AssertionError(
+                    f"Tried to split {opname} based on '.' but found more than 1 '.'"
+                )
+            opname, overload = split
+        else:
+            overload = ""
+
+        if namespace == "higher_order":
+            return
+
+        opname = f"{namespace}::{opname}"
+        if torch._C._get_operation_overload(opname, overload) is not None:
+            # Since we call this when destroying the library, sometimes the
+            # schema will be gone already at that time.
+            schema = torch._C._get_schema(opname, overload)
+            for arg in schema.arguments:
+                if isinstance(arg.type, torch.ClassType):
+                    type_str = arg.type.str()  # pyrefly: ignore[missing-attribute]
+                    if type_str in skip_classes:
+                        continue
+                    self._effect = EffectType.ORDERED
+                    return
+
+    @property
+    def effect(self) -> EffectType | None:
+        return self._effect
+
+    @effect.setter
+    def effect(self, _):
+        raise RuntimeError("Unable to directly set kernel.")
+
+    def register(self, effect: EffectType | None) -> RegistrationHandle:
+        """Register an effect
+
+        Returns a RegistrationHandle that one can use to de-register this
+        effect.
+        """
+        self._effect = effect
+
+        def deregister_effect():
+            self._set_default_effect()
+
+        handle = RegistrationHandle(deregister_effect)
+        return handle