chore: import upstream snapshot with attribution

python/ray/llm/_internal/common/placement.py

@@ -0,0 +1,79 @@
+from typing import List, Literal, Optional
+
+from pydantic import ConfigDict, Field, model_validator
+
+from ray.llm._internal.common.base_pydantic import BaseModelExtended
+
+
+class BundleConfig(BaseModelExtended):
+    """Configuration for a single placement group bundle.
+
+    Resource counts are floats to align with Ray's internal resource
+    representation, which supports fractional values (e.g. GPU=0.5).
+    Both CPU and GPU default to 0.0 — the schema does not inject hidden
+    resource requests. Extra fields are allowed for custom Ray resources (e.g. TPU,
+    accelerator_type:L4).
+    """
+
+    model_config = ConfigDict(extra="allow")
+
+    CPU: float = Field(
+        default=0.0,
+        ge=0.0,
+        description="The number of CPUs per bundle.",
+    )
+    GPU: float = Field(
+        default=0.0,
+        ge=0.0,
+        description="The number of GPUs per bundle.",
+    )
+
+    @model_validator(mode="after")
+    def validate_extra_resources(self):
+        """Ensure custom resources (TPU, accelerator_type, etc.) are non-negative."""
+        extra_resources = self.model_extra
+        if not extra_resources:
+            return self
+        for key, value in extra_resources.items():
+            if not isinstance(value, (int, float)):
+                raise ValueError(
+                    f"Resource '{key}' must be a number, got {type(value).__name__}"
+                )
+            if value < 0:
+                raise ValueError(f"Resource '{key}' must be non-negative, got {value}")
+        return self
+
+
+class PlacementGroupConfig(BaseModelExtended):
+    """Configuration for placement group."""
+
+    bundle_per_worker: Optional[BundleConfig] = Field(
+        default=None,
+        description=(
+            "Resource bundle specification for each worker. "
+            "Auto-replicated based on tensor_parallel_size * pipeline_parallel_size. "
+            "Cannot be used together with 'bundles'."
+        ),
+    )
+    bundles: Optional[List[BundleConfig]] = Field(
+        default=None, description="List of resource bundles"
+    )
+    strategy: Literal["PACK", "SPREAD", "STRICT_PACK", "STRICT_SPREAD"] = Field(
+        default="PACK", description="Placement group strategy"
+    )
+
+    @model_validator(mode="after")
+    def validate_bundle_options(self):
+        if self.bundle_per_worker is not None and self.bundles is not None:
+            raise ValueError(
+                "Cannot specify both 'bundle_per_worker' and 'bundles' in "
+                "placement_group_config. Use 'bundle_per_worker' for simple "
+                "per-worker resource specification (auto-replicated by tp*pp), "
+                "or 'bundles' for full control."
+            )
+        if self.bundle_per_worker is None and self.bundles is None:
+            raise ValueError(
+                "placement_group_config must specify either 'bundle_per_worker' "
+                "or 'bundles'."
+            )
+        return self