"""Utils for ccompatibility issues with verl."""
import os
from logging import Logger
from typing import List
import numpy as np
import torch
from transformers import ProcessorMixin
from verl import DataProto
from verl.trainer.ppo.metric_utils import _compute_response_info
from verl.utils.checkpoint.checkpoint_manager import find_latest_ckpt_path
from trinity.common.config import Config
from trinity.common.experience import (
Experience,
gather_action_masks,
gather_attention_masks,
gather_response_attrs,
gather_token_ids,
split_dpo_experience_to_single_turn,
)
[docs]
def to_data_proto(
experiences: List[Experience], pad_token_id: int, processor: ProcessorMixin, logger: Logger
) -> DataProto: # noqa: C901
"""Convert List[Experience] to verl DataProto."""
assert len(experiences) > 0, "No experiences provided."
if experiences[0].experience_type == "dpo":
experiences = split_dpo_experience_to_single_turn(experiences)
max_prompt_length = max([exp.prompt_length for exp in experiences])
max_response_length = max([len(exp.tokens) - exp.prompt_length for exp in experiences]) # type: ignore
attention_mask = gather_attention_masks(
experiences, max_prompt_length, max_response_length
).long()
cumsum = torch.cumsum(attention_mask, dim=-1)
position_ids = torch.clip(cumsum - 1, 0, None).long()
tokens = gather_token_ids(
experiences, max_prompt_length, max_response_length, pad_token_id
).long()
batch_dict = {
"uid": np.array([exp.eid.tid for exp in experiences]),
"unique_ids": np.array([exp.eid.uid for exp in experiences]),
"position_ids": position_ids,
"input_ids": tokens,
"responses": tokens[:, max_prompt_length:],
"attention_mask": attention_mask,
"response_mask": gather_action_masks(experiences, max_response_length),
}
have_reward = all(exp.reward is not None for exp in experiences)
have_token_level_reward = all(exp.token_level_reward is not None for exp in experiences)
if have_reward or have_token_level_reward:
assert all(exp.logprobs is not None for exp in experiences), "No logprobs provided."
if have_token_level_reward:
if have_reward:
logger.warning(
"Both experiences.rewards and experiences.token_level_rewards are provided. "
"Using experiences.token_level_rewards."
)
token_level_rewards = gather_response_attrs(
experiences, "token_level_reward", max_response_length
)
else:
token_level_rewards = torch.zeros(attention_mask.shape, dtype=torch.float32)
eos_mask_idx = cumsum.argmax(dim=-1)
token_level_rewards[torch.arange(len(experiences)), eos_mask_idx] = torch.tensor(
[exp.reward for exp in experiences],
dtype=torch.float32,
)
token_level_rewards = token_level_rewards[:, max_prompt_length:]
batch_dict.update(
{
"token_level_scores": token_level_rewards,
"rollout_log_probs": gather_response_attrs(
experiences, "logprobs", max_response_length
),
}
)
for attr in ["advantages", "returns", "teacher_logprobs"]:
if all(getattr(exp, attr, None) is not None for exp in experiences):
batch_dict[attr] = gather_response_attrs(experiences, attr, max_response_length)
if processor is not None:
import inspect
# Adapted from verl/experimental/agent_loop/agent_loop.py
position_ids_list, multi_modal_inputs = [], []
for idx, exp in enumerate(experiences):
mm_inputs = exp.multi_modal_inputs or {}
input_ids = batch_dict["input_ids"][idx].unsqueeze(0)
attention_mask = batch_dict["attention_mask"][idx].unsqueeze(0)
get_rope_index_sig = inspect.signature(processor.get_rope_index)
get_rope_index_kwargs = {}
for key in mm_inputs.keys():
if key in get_rope_index_sig.parameters:
get_rope_index_kwargs[key] = mm_inputs[key]
vision_position_ids, _ = processor.get_rope_index(
input_ids=input_ids,
attention_mask=attention_mask,
**get_rope_index_kwargs,
) # (3, 1, seq_len)
vision_position_ids = vision_position_ids.squeeze(1) # (3, seq_len)
text_position_ids = batch_dict["position_ids"][idx].unsqueeze(0) # (1, seq_length)
position_ids = torch.cat(
(text_position_ids, vision_position_ids), dim=0
) # (4, seq_length)
position_ids_list.append(position_ids) # (4, seq_length)
multi_modal_inputs.append(mm_inputs)
batch_dict["position_ids"] = torch.stack(
position_ids_list, dim=0
).long() # (bs, 4, seq_length)
batch_dict["multi_modal_inputs"] = np.array(multi_modal_inputs, dtype=object)
custom_fields_set = set(tuple(exp.custom_fields) for exp in experiences)
if len(custom_fields_set) == 1:
custom_fields = list(custom_fields_set)[0]
for custom_field in custom_fields:
batch_dict[custom_field.destination_field] = torch.tensor(
[exp.info[custom_field.source_field] for exp in experiences],
dtype=custom_field.data_type,
)
else:
raise ValueError("Custom fields are not consistent across experiences.")
meta_info = {
"model_versions": np.array([exp.info.get("model_version", 0) for exp in experiences])
}
return DataProto.from_single_dict(batch_dict, meta_info=meta_info)
[docs]
def compute_data_metrics(batch: DataProto) -> dict:
"""
Computes various metrics from a batch of data for PPO training.
Modified from verl.trainer.ppo.metric_utils.compute_data_metrics
This function calculates metrics related to scores, rewards, advantages, returns, values,
and sequence lengths from a batch of data. It provides statistical information (mean, max, min)
for each metric category.
Args:
batch: A DataProto object containing batch data with token-level scores, rewards, advantages, etc.
Returns:
A dictionary of metrics including:
- critic/score/mean, max, min: Statistics about sequence scores
- critic/rewards/mean, max, min: Statistics about sequence rewards
- critic/advantages/mean, max, min: Statistics about advantages
- critic/returns/mean, max, min: Statistics about returns
- critic/values/mean, max, min: Statistics about critic values
- critic/vf_explained_var: Explained variance of the value function
- response_length/mean, max, min, clip_ratio: Statistics about response lengths
- prompt_length/mean, max, min, clip_ratio: Statistics about prompt lengths
"""
metrics = {}
if "token_level_rewards" in batch.batch and "token_level_scores" in batch.batch:
sequence_score = batch.batch["token_level_scores"].sum(-1)
sequence_reward = batch.batch["token_level_rewards"].sum(-1)
metrics.update(
{
# score
"critic/score/mean": torch.mean(sequence_score).detach().item(),
"critic/score/max": torch.max(sequence_score).detach().item(),
"critic/score/min": torch.min(sequence_score).detach().item(),
# reward
"critic/rewards/mean": torch.mean(sequence_reward).detach().item(),
"critic/rewards/max": torch.max(sequence_reward).detach().item(),
"critic/rewards/min": torch.min(sequence_reward).detach().item(),
}
)
max_response_length = batch.batch["responses"].shape[-1]
prompt_mask = batch.batch["attention_mask"][:, :-max_response_length].bool()
response_mask = batch.batch["attention_mask"][:, -max_response_length:].bool()
max_prompt_length = prompt_mask.size(-1)
response_info = _compute_response_info(batch)
prompt_length = response_info["prompt_length"]
response_length = response_info["response_length"]
metrics.update(
{
# response length
"response_length/mean": torch.mean(response_length).detach().item(),
"response_length/max": torch.max(response_length).detach().item(),
"response_length/min": torch.min(response_length).detach().item(),
"response_length/clip_ratio": torch.mean(
torch.eq(response_length, max_response_length).float()
)
.detach()
.item(),
# prompt length
"prompt_length/mean": torch.mean(prompt_length).detach().item(),
"prompt_length/max": torch.max(prompt_length).detach().item(),
"prompt_length/min": torch.min(prompt_length).detach().item(),
"prompt_length/clip_ratio": torch.mean(
torch.eq(prompt_length, max_prompt_length).float()
)
.detach()
.item(),
}
)
if "advantages" in batch.batch:
# adv
advantages = batch.batch["advantages"]
if response_mask.numel() > 0:
valid_adv = torch.masked_select(advantages, response_mask)
else:
valid_adv = torch.zeros(1)
metrics.update(
{
# adv
"critic/advantages/mean": torch.mean(valid_adv).detach().item(),
"critic/advantages/max": torch.max(valid_adv).detach().item(),
"critic/advantages/min": torch.min(valid_adv).detach().item(),
}
)
if "returns" in batch.batch:
# returns
returns = batch.batch["returns"]
if response_mask.numel() > 0:
valid_returns = torch.masked_select(returns, response_mask)
else:
valid_returns = torch.zeros(1)
metrics.update(
{
"critic/returns/mean": torch.mean(valid_returns).detach().item(),
"critic/returns/max": torch.max(valid_returns).detach().item(),
"critic/returns/min": torch.min(valid_returns).detach().item(),
}
)
return metrics
[docs]
def get_latest_hf_checkpoint_path(config: Config):
"""Get the latest huggingface checkpoint path"""
if config.trainer.trainer_type != "verl":
raise ValueError("This function is only for verl trainer.")
checkpoint_dir = find_latest_ckpt_path(config.checkpoint_job_dir)
hf_checkpoint_dir = os.path.join(checkpoint_dir, "actor", "huggingface")
if not os.path.exists(hf_checkpoint_dir):
raise ValueError(f"No huggingface checkpoint found in {hf_checkpoint_dir}")
return hf_checkpoint_dir
# modified from verl/utils/tokenizer.py:hf_processor
# bug fix for processor
[docs]
def hf_processor(name_or_path, **kwargs):
"""Create a huggingface processor to process multimodal data.
Args:
name_or_path (str): The name of the processor.
Returns:
transformers.ProcessorMixin: The pretrained processor.
"""
import types
import warnings
from transformers import AutoConfig, AutoProcessor
try:
processor = AutoProcessor.from_pretrained(name_or_path, **kwargs)
config = AutoConfig.from_pretrained(name_or_path, **kwargs)
# Bind vlm model's get_rope_index method to processor
processor.config = config
match processor.__class__.__name__:
case "Qwen2VLProcessor":
from transformers.models.qwen2_vl import Qwen2VLModel
processor.get_rope_index = types.MethodType(Qwen2VLModel.get_rope_index, processor)
case "Qwen2_5_VLProcessor":
from transformers.models.qwen2_5_vl import Qwen2_5_VLModel
processor.get_rope_index = types.MethodType(
Qwen2_5_VLModel.get_rope_index, processor
)
case "Qwen3VLProcessor":
from transformers.models.qwen3_vl import Qwen3VLModel
processor.get_rope_index = types.MethodType(Qwen3VLModel.get_rope_index, processor)
case "Glm4vImageProcessor" | "Glm4vProcessor":
from transformers.models.glm4v import Glm4vModel
processor.get_rope_index = types.MethodType(Glm4vModel.get_rope_index, processor)
case "Glm46VProcessor":
from transformers.models.glm46v import Glm46VModel
processor.get_rope_index = types.MethodType(Glm46VModel.get_rope_index, processor)
case _:
raise ValueError(f"Unsupported processor type: {processor.__class__.__name__}")
except Exception as e:
processor = None
# TODO(haibin.lin): try-catch should be removed after adding transformer version req to setup.py to avoid
# silent failure
warnings.warn(
f"Failed to create processor: {e}. This may affect multimodal processing", stacklevel=1
)
# Avoid load tokenizer, see:
# https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/auto/processing_auto.py#L344
if processor is not None and "Processor" not in processor.__class__.__name__:
processor = None
return processor
# modified from verl/utils/fsdp_utils.py:apply_fsdp2
# bug fix for transformers v5
[docs]
def apply_fsdp2(model, fsdp_kwargs, config):
"""model: AutoModelForCausalLM"""
import torch.nn as nn
from verl.utils.fsdp_utils import (
CPUOffloadPolicy,
fully_shard,
maybe_patch_fsdp_module,
)
assert (
CPUOffloadPolicy is not None
), "PyTorch version >= 2.4 is required for using fully_shard API (FSDP2)"
default_transformer_cls_names_to_wrap = getattr(model, "_no_split_modules", None)
fsdp_transformer_layer_cls_to_wrap = config.get("wrap_policy", {}).get(
"transformer_layer_cls_to_wrap", default_transformer_cls_names_to_wrap
)
if isinstance(fsdp_transformer_layer_cls_to_wrap, str):
fsdp_transformer_layer_cls_to_wrap = [fsdp_transformer_layer_cls_to_wrap]
assert len(fsdp_transformer_layer_cls_to_wrap) > 0
modules = []
for name, module in model.named_modules():
if module.__class__.__name__ in fsdp_transformer_layer_cls_to_wrap or (
isinstance(module, nn.Embedding) and not model.config.tie_word_embeddings
):
modules.append(module)
for idx, module in enumerate(modules):
with maybe_patch_fsdp_module(module):
fully_shard(module, **fsdp_kwargs)
with maybe_patch_fsdp_module(model):
fully_shard(model, **fsdp_kwargs) # fsdp2 will not reshard_after_forward for root module
