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Model: ayh015/myLightningOPD
Source: Original Platform

slime/utils/types.py

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+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+
+import torch
+
+
+@dataclass
+class Sample:
+    """The sample generated"""
+
+    group_index: int | None = None
+    index: int | None = None
+    # prompt - can be:
+    #   - str: raw text prompt
+    #   - list[dict[str, str]]: chat messages format
+    prompt: str | list[dict[str, str]] = ""
+    tokens: list[int] = field(default_factory=list)
+    multimodal_inputs: dict[str, Any] = None  # raw multimodal data, e.g. images, videos, etc.
+    multimodal_train_inputs: dict[str, Any] = None  # processed multimodal data, e.g. pixel_values, etc.
+    # response
+    response: str = ""
+    response_length: int = 0
+    label: str | None = None
+    reward: float | dict[str, Any] | None = None
+    loss_mask: list[int] | None = None
+    weight_versions: list[str] = field(default_factory=list)
+    rollout_log_probs: list[float] | None = None  # Log probabilities from rollout engine
+    rollout_routed_experts: list[list[int]] | None = None  # Routed experts from rollout engine
+    remove_sample: bool = False
+
+    class Status(Enum):
+        PENDING = "pending"
+        COMPLETED = "completed"
+        TRUNCATED = "truncated"
+        ABORTED = "aborted"
+        # Indicates a recoverable or non-critical failure during generation (e.g., tool call failure,
+        # external API error, parsing error). Unlike ABORTED, FAILED samples may still contain partial
+        # valid output and can be retried or handled gracefully.
+        FAILED = "failed"
+
+    status: Status = Status.PENDING
+
+    metadata: dict = field(default_factory=dict)
+    # metadata used during training, e.g., what loss to use for this sample.
+    train_metadata: dict | None = None
+
+    class SpecInfo:
+        spec_accept_token_num: int = 0
+        spec_draft_token_num: int = 0
+        spec_verify_ct: int = 0
+        spec_accept_rate: float = 0.0
+        spec_accept_length: float = 0.0
+
+        def add(self, meta_info: dict, response_length: int):
+            self.spec_accept_token_num += meta_info["spec_accept_token_num"]
+            self.spec_draft_token_num += meta_info["spec_draft_token_num"]
+            self.spec_verify_ct += meta_info["spec_verify_ct"]
+            if self.spec_draft_token_num > 0:
+                # Notice: this does not iclude the bonus token generated by verify step.
+                self.spec_accept_rate = self.spec_accept_token_num / self.spec_draft_token_num
+            # self.spec_accept_rate = meta_info["spec_accept_rate"] #
+            if self.spec_verify_ct > 0:
+                self.spec_accept_length = response_length / self.spec_verify_ct
+
+        def to_dict(self):
+            return {
+                "spec_accept_token_num": self.spec_accept_token_num,
+                "spec_draft_token_num": self.spec_draft_token_num,
+                "spec_verify_ct": self.spec_verify_ct,
+                "spec_accept_rate": self.spec_accept_rate,
+                "spec_accept_length": self.spec_accept_length,
+            }
+
+        @staticmethod
+        def from_dict(data: dict):
+            info = Sample.SpecInfo()
+            info.spec_accept_token_num = data.get("spec_accept_token_num", 0)
+            info.spec_draft_token_num = data.get("spec_draft_token_num", 0)
+            info.spec_verify_ct = data.get("spec_verify_ct", 0)
+            info.spec_accept_rate = data.get("spec_accept_rate", 0.0)
+            info.spec_accept_length = data.get("spec_accept_length", 0.0)
+            return info
+
+    spec_info: SpecInfo = field(default_factory=SpecInfo)
+
+    def to_dict(self):
+        value = self.__dict__.copy()
+        value["status"] = self.status.value
+        value["spec_info"] = self.spec_info.to_dict()
+        return value
+
+    @staticmethod
+    def from_dict(data: dict):
+        data["status"] = Sample.Status(data["status"])
+        data["spec_info"] = Sample.SpecInfo.from_dict(data.get("spec_info", {}))
+        return Sample(**data)
+
+    def get_reward_value(self, args) -> float:
+        return self.reward if not args.reward_key else self.reward[args.reward_key]
+
+    @property
+    def effective_response_length(self):
+        return sum(self.loss_mask) if self.loss_mask is not None else self.response_length
+
+
+@dataclass(frozen=True)
+class ParamInfo:
+    name: str
+    dtype: torch.dtype
+    shape: torch.Size
+    attrs: dict
+    size: int
+    src_rank: int
+
+
+# A dict-based batch produced along the rollout -> training path
+# In Megatron backend, several fields are converted to torch.Tensor lists on GPU
+# before being consumed by data iterators (see megatron_utils.actor._get_rollout_data).
+RolloutBatch = dict[str, list[torch.Tensor] | list[int] | list[float] | list[str]]
+
+
+@dataclass
+class MultimodalType:
+    name: str  # Type identifier used in message content (e.g., "image")
+    placeholder: str  # Placeholder token in conversation messages (e.g., "<image>")
+
+
+class MultimodalTypes:
+    IMAGE = MultimodalType(name="image", placeholder="<image>")
+    VIDEO = MultimodalType(name="video", placeholder="<video>")
+    AUDIO = MultimodalType(name="audio", placeholder="<audio>")
+
+    @classmethod
+    def all(cls) -> list[MultimodalType]:
+        return [cls.IMAGE, cls.VIDEO, cls.AUDIO]
+
+    @classmethod
+    def get(cls, name: str) -> MultimodalType | None:
+        return next((m for m in cls.all() if m.name == name), None)