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0185029 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Rollout config
"""
from dataclasses import asdict, dataclass, field
from typing import Any, Optional
@dataclass
class RolloutConfig:
name: str = "vllm"
n: int = 1
temperature: float = 1.0
top_p: float = 1.0
top_k: int = -1
seed: int = 1
limit_images: int = 0
dtype: str = "bf16"
gpu_memory_utilization: float = 0.6
ignore_eos: bool = False
enforce_eager: bool = False
enable_chunked_prefill: bool = False # only for v0 engine
tensor_parallel_size: int = 2
max_model_len: Optional[int] = None
max_num_batched_tokens: int = 8192
disable_log_stats: bool = True
disable_tqdm: bool = False
val_override_config: dict[str, Any] = field(default_factory=dict)
# vLLM KV cache dtype. Decode is HBM-bandwidth-bound on long-prompt RL
# rollouts; switching from "auto" (matches model dtype, bf16=2 bytes/elt)
# to "fp8" (1 byte/elt) halves KV traffic ⇒ ~2× decode speedup on Hopper.
# Quality impact is typically < 0.5 IoU on temporal-grounding tasks
# because attention is a low-rank op; H20 has native FP8 path so no
# software emulation overhead. Choices: "auto" / "fp8" / "fp8_e5m2" /
# "fp8_e4m3". Use "fp8" (vLLM picks the best variant on Hopper).
kv_cache_dtype: str = "auto"
# Return the per-token logprob of every sampled token in
# ``rollout_log_probs`` and let the trainer reuse it as ``old_log_probs``
# instead of recomputing under FSDP, so the first PPO mini-batch update does
# not start from ratio == 1. Incompatible with oracle rows, whose tokens
# differ from what the rollout engine sampled.
calculate_log_probs: bool = False
# Emit the per-sequence mean logprob into
# ``non_tensor_batch["seq_logprob_for_filter"]`` only. It never becomes
# ``old_log_probs``, so it stays out of the PPO ratio path and remains
# compatible with oracle rows.
collect_seq_logprob_for_filter: bool = False
# below are auto keys
prompt_length: int = field(default=-1, init=False)
response_length: int = field(default=-1, init=False)
trust_remote_code: bool = field(default=False, init=False)
def to_dict(self):
return asdict(self)
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